2,718 results
Search Results
2. LSTM-Based Anomaly Detection of Process Instances: Benchmark and Tweaks
- Author
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Lahann, Johannes, Pfeiffer, Peter, Fettke, Peter, van der Aalst, Wil, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Guizzardi, Giancarlo, Series Editor, Montali, Marco, editor, Senderovich, Arik, editor, and Weidlich, Matthias, editor
- Published
- 2023
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3. Correlation-Based Anomaly Detection for the CAN Bus
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Gazdag, András, Lupták, György, Buttyán, Levente, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gelenbe, Erol, editor, Jankovic, Marija, editor, Kehagias, Dionysios, editor, Marton, Anna, editor, and Vilmos, Andras, editor
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- 2022
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4. Anomaly Detection of E-commerce Econnoisseur Based on User Behavior
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Long, Yangyu, Zhao, Wei, Yang, Jilong, Deng, Jincheng, Liu, Fangming, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Lu, Wei, editor, Zhang, Yuqing, editor, Wen, Weiping, editor, Yan, Hanbing, editor, and Li, Chao, editor
- Published
- 2022
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5. Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI
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Meissen, Felix, Kaissis, Georgios, Rueckert, Daniel, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor
- Published
- 2022
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6. Active Anomaly Detection for Key Item Selection in Process Auditing
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Post, Ruben, Beerepoot, Iris, Lu, Xixi, Kas, Stijn, Wiewel, Sebastiaan, Koopman, Angelique, Reijers, Hajo, van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Munoz-Gama, Jorge, editor, and Lu, Xixi, editor
- Published
- 2022
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7. PErrCas: Process Error Cascade Mining in Trace Streams
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Wimbauer, Anna, Richter, Florian, Seidl, Thomas, van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Munoz-Gama, Jorge, editor, and Lu, Xixi, editor
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- 2022
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8. Deep Learning Based Anomaly Detection for Muti-dimensional Time Series: A Survey
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Chen, Zhipeng, Peng, Zhang, Zou, Xueqiang, Sun, Haoqi, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Lu, Wei, editor, Zhang, Yuqing, editor, Wen, Weiping, editor, Yan, Hanbing, editor, and Li, Chao, editor
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- 2022
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9. Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach
- Author
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Maris Bauer, Raphael Hussung, Carsten Matheis, Hermann Reichert, Peter Weichenberger, Jens Beck, Uwe Matuschczyk, Joachim Jonuscheit, and Fabian Friederich
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terahertz imaging ,nondestructive testing ,frequency-modulated continuous wave ,press sleeves ,paper industry ,anomaly detection ,Chemical technology ,TP1-1185 - Abstract
We present a rotational terahertz imaging system for inline nondestructive testing (NDT) of press sleeves for the paper industry during fabrication. Press sleeves often consist of polyurethane (PU) which is deposited by rotational molding on metal barrels and its outer surface mechanically processed in several milling steps afterwards. Due to a stabilizing polyester fiber mesh inlay, small defects can form on the sleeve’s backside already during the initial molding, however, they cannot be visually inspected until the whole production processes is completed. We have developed a fast-scanning frequenc-modulated continuous wave (FMCW) terahertz imaging system, which can be integrated into the manufacturing process to yield high resolution images of the press sleeves and therefore can help to visualize hidden structural defects at an early stage of fabrication. This can save valuable time and resources during the production process. Our terahertz system can record images at 0.3 and 0.5 THz and we achieve data acquisition rates of at least 20 kHz, exploiting the fast rotational speed of the barrels during production to yield sub-millimeter image resolution. The potential of automated defect recognition by a simple machine learning approach for anomaly detection is also demonstrated and discussed.
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- 2021
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10. Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach
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Fabian Friederich, Raphael Hussung, Carsten Matheis, Joachim Jonuscheit, Maris Bauer, Uwe Matuschczyk, Peter Weichenberger, Jens Beck, Hermann Reichert, and Publica
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Fabrication ,Terahertz radiation ,Computer science ,TP1-1185 ,Molding (process) ,Biochemistry ,Article ,Rotational molding ,Analytical Chemistry ,Machine Learning ,terahertz imaging ,frequency-modulated continuous wave ,Data acquisition ,Nondestructive testing ,Electrical and Electronic Engineering ,Instrumentation ,Image resolution ,nondestructive testing ,business.industry ,Chemical technology ,Pulp and paper industry ,anomaly detection ,Atomic and Molecular Physics, and Optics ,paper industry ,Anomaly detection ,press sleeves ,business - Abstract
We present a rotational terahertz imaging system for inline nondestructive testing (NDT) of press sleeves for the paper industry during fabrication. Press sleeves often consist of polyurethane (PU) which is deposited by rotational molding on metal barrels and its outer surface mechanically processed in several milling steps afterwards. Due to a stabilizing polyester fiber mesh inlay, small defects can form on the sleeve’s backside already during the initial molding, however, they cannot be visually inspected until the whole production processes is completed. We have developed a fast-scanning frequenc-modulated continuous wave (FMCW) terahertz imaging system, which can be integrated into the manufacturing process to yield high resolution images of the press sleeves and therefore can help to visualize hidden structural defects at an early stage of fabrication. This can save valuable time and resources during the production process. Our terahertz system can record images at 0.3 and 0.5 THz and we achieve data acquisition rates of at least 20 kHz, exploiting the fast rotational speed of the barrels during production to yield sub-millimeter image resolution. The potential of automated defect recognition by a simple machine learning approach for anomaly detection is also demonstrated and discussed.
- Published
- 2021
11. Optimizing feature selection in intrusion detection systems: Pareto dominance set approaches with mutual information and linear correlation.
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Barbosa, Guilherme Nunes Nasseh, Andreoni, Martin, and Mattos, Diogo Menezes Ferrazani
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FEATURE selection ,INTRUSION detection systems (Computer security) ,MACHINE learning ,SOCIAL dominance ,PEARSON correlation (Statistics) ,FILTER paper - Abstract
In the realm of network intrusion detection using machine learning, feature selection aims for computational efficiency, enhanced performance, and model interpretability, preventing overfitting and optimizing data visualization. This paper proposes a filtering method for feature selection, which optimizes information quantity and linear correlation between resultant features. The method identifies Pareto dominant pairs of informative and correlated features, constructs a graph, and selects key features based on betweenness centrality in its connected components. The proposal yields a more concise and informative dataset representation. Experimental results, using three diverse datasets, demonstrate that the proposal achieves more than 95% accuracy in classifying network attacks with just 14% of the total number features in original datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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12. Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach.
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Bauer, Maris, Hussung, Raphael, Matheis, Carsten, Reichert, Hermann, Weichenberger, Peter, Beck, Jens, Matuschczyk, Uwe, Jonuscheit, Joachim, and Friederich, Fabian
- Subjects
- *
SUBMILLIMETER wave imaging , *MACHINE learning , *NONDESTRUCTIVE testing , *TERAHERTZ technology , *PAPER industry , *HIGH resolution imaging , *MOLDING (Founding) - Abstract
We present a rotational terahertz imaging system for inline nondestructive testing (NDT) of press sleeves for the paper industry during fabrication. Press sleeves often consist of polyurethane (PU) which is deposited by rotational molding on metal barrels and its outer surface mechanically processed in several milling steps afterwards. Due to a stabilizing polyester fiber mesh inlay, small defects can form on the sleeve's backside already during the initial molding, however, they cannot be visually inspected until the whole production processes is completed. We have developed a fast-scanning frequenc-modulated continuous wave (FMCW) terahertz imaging system, which can be integrated into the manufacturing process to yield high resolution images of the press sleeves and therefore can help to visualize hidden structural defects at an early stage of fabrication. This can save valuable time and resources during the production process. Our terahertz system can record images at 0.3 and 0.5 THz and we achieve data acquisition rates of at least 20 kHz, exploiting the fast rotational speed of the barrels during production to yield sub-millimeter image resolution. The potential of automated defect recognition by a simple machine learning approach for anomaly detection is also demonstrated and discussed. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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13. Using AI to detect panic buying and improve products distribution amid pandemic.
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Adulyasak, Yossiri, Benomar, Omar, Chaouachi, Ahmed, Cohen, Maxime C., and Khern-am-nuai, Warut
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CONSUMER behavior ,COVID-19 pandemic ,PRODUCT improvement ,ARTIFICIAL intelligence ,COVID-19 - Abstract
The COVID-19 pandemic has triggered panic-buying behavior around the globe. As a result, many essential supplies were consistently out-of-stock at common point-of-sale locations. Even though most retailers were aware of this problem, they were caught off guard and are still lacking the technical capabilities to address this issue. The primary objective of this paper is to develop a framework that can systematically alleviate this issue by leveraging AI models and techniques. We exploit both internal and external data sources and show that using external data enhances the predictability and interpretability of our model. Our data-driven framework can help retailers detect demand anomalies as they occur, allowing them to react strategically. We collaborate with a large retailer and apply our models to three categories of products using a dataset with more than 15 million observations. We first show that our proposed anomaly detection model can successfully detect anomalies related to panic buying. We then present a prescriptive analytics simulation tool that can help retailers improve essential product distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help retailers increase access to essential products by 56.74%. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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14. An anomaly detection method based on double encoder–decoder generative adversarial networks
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Liu, Hui, Tang, Tinglong, Luo, Jake, Zhao, Meng, Zheng, Baole, and Wu, Yirong
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- 2021
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15. Signature-based Adaptive Cloud Resource Usage Prediction Using Machine Learning and Anomaly Detection.
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Sus, Wiktor and Nawrocki, Piotr
- Abstract
One of the challenges in managing cloud computing clusters is assigning resources based on the customers’ needs. For this mechanism to work efficiently, it is imperative that there are sufficient resources reserved to maintain continuous operation, but not too much to avoid overhead costs. Additionally, to avoid the overhead of acquisition time, it is important to reserve resources sufficiently in advance. This paper presents a novel reliable general-purpose mechanism for prediction-based resource usage reservation. The proposed solution should be capable of operating for long periods of time without drift-related problems, and dynamically adapt to changes in system usage. To achieve this, a novel signature-based ensemble prediction method is presented, which utilizes multiple distinct prediction algorithms suited for various use-cases, as well as an anomaly detection mechanism used to improve prediction accuracy. This ensures that the mechanism can operate efficiently in different real-life scenarios. Thanks to a novel signature-based selection algorithm, it is possible to use the best available prediction algorithm for each use-case, even over long periods of time, which would typically lead to drifts. The proposed approach has been evaluated using real-life historical data from various production servers, which include traces from more than 1,500 machines collected over more than a year. Experimental results have demonstrated an increase in prediction accuracy of up to 21.4 percent over the neural network approach. The evaluation of the proposed approach highlights the importance of choosing the appropriate prediction method, especially in diverse scenarios where the load changes frequently. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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16. Anomalous process detection for Internet of Things based on K-Core.
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Yue Chang, Teng Hu, Fang Lou, Tao Zeng, Mingyong Yin, Siqi Yang, Shaowei Wang, and Sheng Chen
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INTERNET of things ,INTRUSION detection systems (Computer security) ,COMPUTER security ,ARTIFICIAL intelligence ,DISTRIBUTED computing ,OPTIMIZATION algorithms - Abstract
In recent years, Internet of Things security incidents occur frequently, which is often accompanied by malicious events. Therefore, anomaly detection is an important part of Internet of Things security defense. In this paper, we create a process whitelist based on the K-Core decomposition method for detecting anomalous processes in IoT devices. The method first constructs an IoT process network according to the relationships between processes and IoT devices. Subsequently, it creates a whitelist and detect anomalous processes. Our work innovatively transforms process data into a network framework, employing K-Core analysis to identify core processes that signify high popularity. Then, a threshold-based filtering mechanism is applied to formulate the process whitelist. Experimental results show that the unsupervised method proposed in this paper can accurately detect anomalous processes on real-world datasets. Therefore, we believe our algorithm can be widely applied to anomaly process detection, ultimately enhancing the overall security of the IoT. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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17. Electricity Behavior Modeling and Anomaly Detection Services Based on a Deep Variational Autoencoder Network.
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Lin, Rongheng, Chen, Shuo, He, Zheyu, Wu, Budan, Zou, Hua, Zhao, Xin, and Li, Qiushuang
- Abstract
Understanding electrical load profiles and detecting anomaly behaviors are important to the smart grid system. However, current load identification and anomaly analysis are based on static analysis, and less consideration is given to anomaly findings under load change conditions. This paper proposes a deep variational autoencoder network (DVAE) for load profiles, along with anomaly analysis services, and introduces auto-time series data updating strategies based on sliding window adjustment. DVAE can help reconstruct the load curve and measure the difference between the original and the newer curve, whose measurement indicators include reconstruction probability and Pearson similarity. Meanwhile, the design of the sliding window strategy updates the data and DVAE model in a time-series manner. Experiments were carried out based on datasets from the U.S. Department of Energy and from Southeast China. The results showed that the proposed services could result in a 5% improvement in the AUC value, which helps to identify the anomaly behavior. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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18. A survey on social network's anomalous behavior detection.
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Xing, Ling, Li, Shiyu, Zhang, Qi, Wu, Honghai, Ma, Huahong, and Zhang, Xiaohui
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SOCIAL networks ,ANOMALY detection (Computer security) ,SOCIAL media ,RESEARCH personnel - Abstract
The onset of Web 3.0 has catalyzed the rapid advancement of social networking, transforming platforms into essential elements deeply embedded within the fabric of daily life. Researchers have proposed several methods for detecting anomalous behaviors in various scenarios. This article provides a comprehensive review of current research and the latest developments in anomalous behavior detection within social networks. We present a hierarchical three-layer categorization scheme based on the distinct characteristics of base-level detection technologies and various datasets. First, anomaly detection based on user behavioral characteristics can intuitively reflect deviations in individual behavior. However, it may overlook the overall network structure's impact. Second, detecting anomalies within a network's topological structure highlights structural significance, but may overlook the subtle nuances of individual behavior. Finally, the coordinated fusion method, which blends individual behavioral characteristics and the network's topological structure, addresses the multifaceted nature of anomalies, yielding a more thorough and accurate anomaly detection strategy. This paper provides an overview and assesses the performance of three anomaly detection methods. Furthermore, we explore the challenges associated with social network anomaly detection and the potential pathways for further research. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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19. Beyond Supervised: The Rise of Self-Supervised Learning in Autonomous Systems.
- Author
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Taherdoost, Hamed
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SUPERVISED learning ,IMAGE analysis ,DIAGNOSTIC imaging ,FEATURE extraction ,INSTRUCTIONAL systems ,SCALABILITY - Abstract
Supervised learning has been the cornerstone of many successful medical imaging applications. However, its reliance on large labeled datasets poses significant challenges, especially in the medical domain, where data annotation is time-consuming and expensive. In response, self-supervised learning (SSL) has emerged as a promising alternative, leveraging unlabeled data to learn meaningful representations without explicit supervision. This paper provides a detailed overview of supervised learning and its limitations in medical imaging, underscoring the need for more efficient and scalable approaches. The study emphasizes the importance of the area under the curve (AUC) as a key evaluation metric in assessing SSL performance. The AUC offers a comprehensive measure of model performance across different operating points, which is crucial in medical applications, where false positives and negatives have significant consequences. Evaluating SSL methods based on the AUC allows for robust comparisons and ensures that models generalize well to real-world scenarios. This paper reviews recent advances in SSL for medical imaging, demonstrating their potential to revolutionize the field by mitigating challenges associated with supervised learning. Key results show that SSL techniques, by leveraging unlabeled data and optimizing performance metrics like the AUC, can significantly improve the diagnostic accuracy, scalability, and efficiency in medical image analysis. The findings highlight SSL's capability to reduce the dependency on labeled datasets and present a path forward for more scalable and effective medical imaging solutions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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20. Leak Event Diagnosis for Power Plants: Generative Anomaly Detection Using Prototypical Networks.
- Author
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Jeong, Jaehyeok, Yeo, Doyeob, Roh, Seungseo, Jo, Yujin, and Kim, Minsuk
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ANOMALY detection (Computer security) ,ARTIFICIAL intelligence ,LEAK detection ,ACQUISITION of data ,NOZZLES - Abstract
Anomaly detection systems based on artificial intelligence (AI) have demonstrated high performance and efficiency in a wide range of applications such as power plants and smart factories. However, due to the inherent reliance of AI systems on the quality of training data, they still demonstrate poor performance in certain environments. Especially in hazardous facilities with constrained data collection, deploying these systems remains a challenge. In this paper, we propose Generative Anomaly Detection using Prototypical Networks (GAD-PN) designed to detect anomalies using only a limited number of normal samples. GAD-PN is a structure that integrates CycleGAN with Prototypical Networks (PNs), learning from metadata similar to the target environment. This approach enables the collection of data that are difficult to gather in real-world environments by using simulation or demonstration models, thus providing opportunities to learn a variety of environmental parameters under ideal and normal conditions. During the inference phase, PNs can classify normal and leak samples using only a small number of normal data from the target environment by prototypes that represent normal and abnormal features. We also complement the challenge of collecting anomaly data by generating anomaly data from normal data using CycleGAN trained on anomaly features. It can also be adapted to various environments that have similar anomalous scenarios, regardless of differences in environmental parameters. To validate the proposed structure, data were collected specifically targeting pipe leakage scenarios, which are significant problems in environments such as power plants. In addition, acoustic ultrasound signals were collected from the pipe nozzles in three different environments. As a result, the proposed model achieved a leak detection accuracy of over 90% in all environments, even with only a small number of normal data. This performance shows an average improvement of approximately 30% compared with traditional unsupervised learning models trained with a limited dataset. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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21. Understanding Regulatory Changes: Deep Learning in Sustainable Finance and Banking.
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Anghel, Bogdan Ionut and Lupu, Radu
- Subjects
CONVOLUTIONAL neural networks ,SUSTAINABLE investing ,DEEP learning ,BANKING industry ,ANOMALY detection (Computer security) - Abstract
This paper examines the regulatory impact on the European Banking Sector using advanced deep learning techniques to analyze the relationship between Sustainable Finance guidelines and the SX7P Index from January 2012 to December 2023. Utilizing Long Short-Term Memory Auto-encoder (LSTM-AE), Variational Autoencoder (VAE), and Convolutional Neural Network (CNN) for anomaly detection, the study compares anomalies and investigates their correlation with European Banking Authority (EBA) events and Sustainable Finance guidelines from January 2020 to December 2023. Through the analysis of 43 pertinent EBA documents, the research identifies patterns and variations in anomalies, assessing their association with regulatory changes. The results reveal significant anomalies aligning with regulatory events, indicating a potential causal relationship. Notably, the VAE methodology shows the strongest correlation between EBA Sustainable Finance events and anomalies. This research advances the understanding of deep learning applications in financial markets and offers valuable insights for policymakers and financial institutions regarding regulatory shifts in Sustainable Finance. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
22. A review of intrusion detection system and security threat in internet of things enabled environment.
- Author
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Nisha, Gill, Nasib Singh, and Gulia, Preeti
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INTERNET of things ,INTERNET security ,SECURITY systems ,INTRUSION detection systems (Computer security) ,TELECOMMUNICATION systems ,SENSOR networks - Abstract
Thousands of devices communicate globally to share data and information without any human intervention. A network of physical objects with numerous sensors and other network hardware to exchange data with servers and additional devices that are linked is referred to as the "internet of things (IoT)". The actions hurting the communication system are known as intrusions. Security features such as (integrity, and confidentiality) within IoT networks are compromised when any kind of intrusion occurs. To identify multiple infiltration types in an environment where IoT is enabled, an intrusion detection system (IDS) is required. In environments where IoT is enabled, security vulnerabilities are now more prevalent than ever. In this study, the IoT architecture is reviewed, and potential security risks at each tier are investigated. It is also hoped that this research will stimulate thought about the expanding risks posed by unprotected IoT devices. The paper also intends to provide an in-depth analysis of intrusion detection systems for identifying and classifying security threats in an IoT-enabled environment. Furthermore, this study investigates a variety of efficient machine learning-based methods for detecting cyberattacks on IoT devices. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
23. Artificial intelligence powered internet of vehicles: securing connected vehicles in 6G.
- Author
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Raja, Depa Ramachandraiah Kumar, Abas, Zuraida Abal, Akula, Chandra Sekhar, Kumar, Yellapalli Dileep, Kumar, Goshtu Hemanth, and Eswari, Venappagari
- Subjects
ARTIFICIAL intelligence ,DEEP learning ,INTERNET ,SIMPLE machines ,5G networks ,MACHINE learning - Abstract
The rapid advancements in automotive technology and the emergence of next-generation networks such as 5G and 6G are laying the foundation for the internet of vehicles (IoV), a revolutionary concept to transform transportation systems. The convergence of artificial intelligence (AI) and connected vehicles IoV is driving a paradigm shift in the transportation sector, especially in the dynamic framework of 5G and future 6G networks. This survey paper provides a thorough survey of the evolving AI-based IoV security landscape. We explore key areas of 5G/6G networks, focusing on the complex interplay of machine learning (ML) and deep learning (DL) in enhancing vehicle-to-everything (V2X) security and connected vehicles. Addressing the unique challenges of 6G, this paper outlines future directions for improving security and highlights open research issues. This comprehensive survey, which aims to provide information and guidance to both researchers and practitioners, contributes to a detailed understanding of the security issues associated with connected vehicles in the emerging 6G era. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
24. Real-Time Monitoring of Data Pipelines: Exploring and Experimentally Proving that the Continuous Monitoring in Data Pipelines Reduces Cost and Elevates Quality.
- Author
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Narayanan, Shammy, S., Maheswari, and Zephan, Prisha
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DATA integrity ,DATA quality ,PIPELINE inspection ,ELECTRONIC data processing ,PIPELINE failures ,COST ,ELECTRONIC commerce - Abstract
Data pipelines are crucial for processing and transforming data in various domains, including finance, healthcare, and ecommerce. Ensuring the reliability and accuracy of data pipelines is of utmost importance to maintain data integrity and make informed business decisions. In this paper, we explore the significance of continuous monitoring in data pipelines and its contribution to data observability. This work discusses the challenges associated with monitoring data pipelines in realtime, propose a framework for real-time monitoring, and highlight its benefits in enhancing data observability. The findings of this work emphasize the need for organizations to adopt continuous monitoring practices to ensure data quality, detect anomalies, and improve overall system performance. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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25. RAMFAE: a novel unsupervised visual anomaly detection method based on autoencoder.
- Author
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Sun, Zhongju, Wang, Jian, and Li, Yakun
- Abstract
Traditional methods of visual anomaly detection based on reconstruction often use normal data to train autoencoder. Then the metric distance detection method is used to estimate whether the samples of detection belong to the exception class. However, this method has some problems that the autoencoder produces blurry images to cause false detection of normal pixel points. The model may still be able to fully reconstruct the undiscovered defects due to the large capacity of autoencoder, even if it is trained only on normal samples. Then, the metric distance detection method would ignore local key information. To solve this problem, this paper comes up with the random anomaly multi-scale feature focused autoencoder (RAMFAE), an innovative unsupervised visual anomaly detection technique, which incorporates three novel concepts. First, a multi-scale feature focused extraction (MFFE) network structure is designed and added between the encoder and decoder, which effectively solves the problem of reconstructing image blur and effectively improves the sensitivity of the model to normal regions. Second, this article employs Delete Paste, a novel data augmentation strategy for generating two different types of random anomalies, which pastes the cut part into a random location, while the pixels in the original position are filled with 0. In spite of the input anomalous images, the strategy makes the model be able to produce normal images to avoid the phenomenon of anomaly reconstruction, and then enables defect localization based on the error between the measured image and the reconstructed image. Third, the study adopts the image quality assessment with combining gradient magnitude similarity deviation (GMSD) and structural similarity (SSIM) to solve the problem that local key information and texture detail information are not easy to be paid attention to by the model, and alleviate the training pressure caused by Delete Paste enhancement. We perform an extensive evaluation on the challenging MVTec AD data set and compare it with the advanced visual anomaly detection methods in recent years as well. The AUC final result of RAMFAE in this text reaches 94.5, which is 3.6, 2.5 and 0.8 higher than the advanced IGD, FCDD and RIAD detection methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
26. Deep Graph Learning for Anomalous Citation Detection.
- Author
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Liu, Jiaying, Xia, Feng, Feng, Xu, Ren, Jing, and Liu, Huan
- Subjects
CITATION networks ,ANOMALY detection (Computer security) ,PUBLIC safety ,TEXT mining - Abstract
Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to scholarly data, that is, anomaly detection in a citation network. Citation is considered as one of the most crucial metrics to evaluate the impact of scientific research, which may be gamed in multiple ways. Therefore, anomaly detection in citation networks is of significant importance to identify manipulation and inflation of citations. To address this open issue, we propose a novel deep graph learning model, namely graph learning for anomaly detection (GLAD), to identify anomalies in citation networks. GLAD incorporates text semantic mining to network representation learning by adding both node attributes and link attributes via graph neural networks (GNNs). It exploits not only the relevance of citation contents, but also hidden relationships between papers. Within the GLAD framework, we propose an algorithm called Citation PUrpose (CPU) to discover the purpose of citation based on citation context. The performance of GLAD is validated through a simulated anomalous citation dataset. Experimental results demonstrate the effectiveness of GLAD on the anomalous citation detection task. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
27. SPACECRAFT TEST DATA INTEGRATION MANAGEMENT TECHNOLOGY BASED ON BIG DATA PLATFORM.
- Subjects
BIG data ,DATA integration ,DATA management ,SCANNING tunneling microscopy ,SHORT-term memory ,LONG-term memory ,SPACE vehicles - Abstract
In this paper, a general test platform for spacecraft data management is designed and constructed. This paper introduces a portable software development environment based on LUA. The technology of space environment data management, comprehensive analysis, parameter correction and visual display of spacecraft is realized. The relationship between continuity, mixed dispersion, variation and indication of remote sensing data is studied. This project uses the integrated Long Short Term Memory network (LSTM) technology to detect anomalies in satellite remote sensing observation data. Give full play to the advantages of laser scanning tunneling microscope in the nonlinear field. The combination of this method and the matrix method can improve the adaptive ability of spacecraft in an operation state to better identify abnormal information in remote sensing data. Experiments show that the algorithm can significantly improve the anomaly detection rate of the system. The system can monitor the front test device and record the data. The method can be connected with the space vehicle's central control and automatic test system. The comprehensive management of the integrated test system of space vehicles is realized. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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28. Insights into Modern Intrusion Detection Strategies for Internet of Things Ecosystems.
- Author
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Isong, Bassey, Kgote, Otshepeng, and Abu-Mahfouz, Adnan
- Subjects
INTERNET of things ,CYBERTERRORISM ,ECOSYSTEMS ,BLOCKCHAINS ,MACHINE learning ,COMPUTER network security - Abstract
The swift explosion of Internet of Things (IoT) devices has brought about a new era of interconnectivity and ease of use while simultaneously presenting significant security concerns. Intrusion Detection Systems (IDS) play a critical role in the protection of IoT ecosystems against a wide range of cyber threats. Despite research advancements, challenges persist in improving IDS detection accuracy, reducing false positives (FPs), and identifying new types of attacks. This paper presents a comprehensive analysis of recent developments in IoT, shedding light on detection methodologies, threat types, performance metrics, datasets, challenges, and future directions. We systematically analyze the existing literature from 2016 to 2023, focusing on both machine learning (ML) and non-ML IDS strategies involving signature, anomaly, specification, and hybrid models to counteract IoT-specific threats. The findings include the deployment models from edge to cloud computing and evaluating IDS performance based on measures such as accuracy, FP rates, and computational costs, utilizing various IoT benchmark datasets. The study also explores methods to enhance IDS accuracy and efficiency, including feature engineering, optimization, and cutting-edge solutions such as cryptographic and blockchain technologies. Equally, it identifies key challenges such as the resource-constrained nature of IoT devices, scalability, and privacy issues and proposes future research directions to enhance IoT-based IDS and overall ecosystem security. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
29. Anomaly‐background separation and particle swarm optimization based band selection for hyperspectral anomaly detection.
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Shang, Xiaodi, Duan, Yiqi, Wang, Xiaopeng, Fu, Baijia, and Sun, Xudong
- Subjects
PARTICLE swarm optimization ,INTRUSION detection systems (Computer security) ,DISTRIBUTION (Probability theory) ,SURFACE analysis - Abstract
As one of the dimensionality reduction techniques of hyperspectral image (HSI), band selection (BS) does not change the spectral characteristics and physical meaning of HSIs, which is beneficial to the identification and analysis of surface objects. Recently, many BS methods for target detection have achieved promising results by making full use of the priori spectral features of the target to be detected. Conversely, anomaly detection separates the anomaly based solely on the statistical distribution difference between anomaly and background without any prior information. Therefore, the development of BS for anomaly detection has lagged far behind that of BS for target detection. To this end, this paper proposes a novel BS algorithm dedicated to anomaly detection tasks, named anomaly‐background separation and particle swarm optimization (PSO)‐based BS. Specifically, an anomaly‐background separation framework (ABSF) is established to predetermine a priori knowledge of anomaly distribution. Then, three band prioritization criteria are constructed with the anomaly‐background constraints generated by ABSF. Finally, PSO is used to find the optimal subset of bands in the solution space. The experiments on two real datasets demonstrate that the proposed method yields better detection results and greater stability compared to other BS methods discussed in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
30. GAN-Based Anomaly Detection Tailored for Classifiers.
- Author
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Králik, Ľubomír, Kontšek, Martin, Škvarek, Ondrej, and Klimo, Martin
- Subjects
GENERATIVE adversarial networks ,ARTIFICIAL neural networks ,DATABASES - Abstract
Pattern recognition systems always misclassify anomalies, which can be dangerous for uninformed users. Therefore, anomalies must be filtered out from each classification. The main challenge for the anomaly filter design is the huge number of possible anomaly samples compared with the number of samples in the training set. Tailoring the filter for the given classifier is just the first step in this reduction. Paper tests the hypothesis that the filter trained in avoiding "near" anomalies will also refuse the "far" anomalies, and the anomaly detector is then just a classifier distinguishing between "far real" and "near anomaly" samples. As a "far real" samples generator was used, a Generative Adversarial Network (GAN) fake generator that transforms normally distributed random seeds into fakes similar to the training samples. The paper proves the assumption that seeds unused in fake training will generate anomalies. These seeds are distinguished according to their Chebyshev norms. While the fakes have seeds within the hypersphere with a given radius, the near anomalies have seeds within the sphere near cover. Experiments with various anomaly test sets have shown that GAN-based anomaly detectors create a reliable anti-anomaly shield using the abovementioned assumptions. The proposed anomaly detector is tailored to the given classifier, but its limitation is due to the need for the availability of the database on which the classifier was trained. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
31. An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder.
- Author
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Zhou, Shenghan, He, Zhao, Chen, Xu, and Chang, Wenbing
- Subjects
INTRUSION detection systems (Computer security) ,DECOMPOSITION method ,FEATURE extraction ,DEEP learning ,DRONE aircraft ,DATA extraction - Abstract
The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed anomaly detection method with wavelet decomposition and stacked denoising autoencoder methods. Anomaly detection based on UAV flight data is an important method of UAV condition monitoring and potential abnormal state mining, which is an important means to reduce the risk of UAV flight accidents. However, the diversity of UAV mission scenarios leads to a complex and harsh environment, so the acquired data are affected by noise, which brings challenges to accurate anomaly detection based on UAV data. Firstly, we use wavelet decomposition to denoise the original data; then, we used the stacked denoising autoencoder to achieve feature extraction. Finally, the softmax classifier is used to realize the anomaly detection of UAV. The experimental results demonstrate that the proposed method still has good performance in the case of noisy data. Specifically, the Accuracy reaches 97.53%, the Precision is 97.50%, the Recall is 91.81%, and the F1-score is 94.57%. Furthermore, the proposed method outperforms the four comparison models with more outstanding performance. Therefore, it has significant potential in reducing UAV flight accidents and enhancing operational safety. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
32. Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey.
- Author
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Cholevas, Christos, Angeli, Eftychia, Sereti, Zacharoula, Mavrikos, Emmanouil, and Tsekouras, George E.
- Subjects
DATA structures ,MACHINE learning ,PRIVATE networks ,BLOCKCHAINS ,ALGORITHMS - Abstract
In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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33. Unsupervised Anomaly Detection of Industrial Images Based on Dual Generator Reconstruction Networks.
- Author
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Cong Gu, Siyv Ren, and Qiqiang Duan
- Subjects
ANOMALY detection (Computer security) ,DEEP learning - Abstract
At present, deep learning techniques are increasingly utilized in computer vision and anomaly detection. To address the limitations of inadequate reconstruction capability and subpar performance in reconstruction-based anomaly detection, this study enhances the existing algorithm and introduces an unsupervised anomaly detection of industrial images algorithm based on dual generator reconstruction networks-DGRNet. The network consists of two generators and a discriminator, introducing a widely recognized denoising diffusion probabilistic model (DDPM) as one of the generators, an autoencoder (AE) as the other generator, and a decoder as the discriminator. The model is tested on the MVTec AD dataset, and in the case of no additional training data, the anomaly detection AUC result of DGRNet exceeds the baseline method based on reconstruction by 19.6 percentage points. The experimental results show that DGRNet can improve the detection performance in the anomaly detection algorithm based on unsupervised and reconstructed networks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
34. Using Ensemble Learning for Anomaly Detection in Cyber–Physical Systems.
- Author
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Jeffrey, Nicholas, Tan, Qing, and Villar, José R.
- Subjects
CYBER physical systems ,INTRUSION detection systems (Computer security) ,INFORMATION technology ,MACHINE learning ,TECHNOLOGY convergence ,SYSTEMS availability ,DEEP learning - Abstract
The swift embrace of Industry 4.0 paradigms has led to the growing convergence of Information Technology (IT) networks and Operational Technology (OT) networks. Traditionally isolated on air-gapped and fully trusted networks, OT networks are now becoming more interconnected with IT networks due to the advancement and applications of IoT. This expanded attack surface has led to vulnerabilities in Cyber–Physical Systems (CPSs), resulting in increasingly frequent compromises with substantial economic and life safety repercussions. The existing methods for the anomaly detection of security threats typically use simple threshold-based strategies or apply Machine Learning (ML) algorithms to historical data for the prediction of future anomalies. However, due to the high levels of heterogeneity across different CPS environments, minimizing the opportunities for transfer learning, and the scarcity of real-world data for training, the existing ML-based anomaly detection techniques suffer from a poor predictive performance. This paper introduces a hybrid anomaly detection approach designed to identify threats to CPSs by combining the signature-based anomaly detection typically utilized in IT networks, the threshold-based anomaly detection typically utilized in OT networks, and behavioural-based anomaly detection using Ensemble Learning (EL), which leverages the strengths of multiple ML algorithms against the same dataset to increase the accuracy. Multiple public research datasets were used to validate the proposed approach, with the hybrid methodology employing a divide-and-conquer strategy to offload the detection of certain cyber threats to computationally inexpensive signature-based and threshold-based methods using domain knowledge to minimize the size of the behavioural-based data needed for ML model training, thus achieving a higher accuracy over a reduced timeframe. The experimental results showed accuracy improvements of 4–7% over those of the conventional ML classifiers in performing anomaly detection across multiple datasets, which is particularly important to the operators of CPS environments due to the high financial and life safety costs associated with interruptions to system availability. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
35. Metaheuristic-based time series clustering for anomaly detection in manufacturing industry.
- Author
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Suh, Woong Hyun, Oh, Sanghoun, and Ahn, Chang Wook
- Subjects
TIME series analysis ,ANOMALY detection (Computer security) ,MANUFACTURING industries ,METAHEURISTIC algorithms ,GENETIC algorithms ,DISTRIBUTION (Probability theory) - Abstract
Nowadays time series clustering is of great importance in manufacturing industries. Meanwhile, it is considerably challenging to achieve explainable solution as well as significant performance due to computation complexity and variable diversity. To efficaciously handle the difficulty, this paper presents a novel metaheuristic-based time series clustering method which can improve the effectiveness and logicality of existing clustering approaches. The proposed method collects candidate cluster references from hierarchical and partitional clustering through shape-based distance measure as well as dynamic time warping (DTW) on manufacturing time series data. By applying metaheuristics highlighting estimation of distribution algorithms (EDA), such as extended compact genetic algorithm (ECGA), on the collected candidate clusters, advanced cluster centroid combinations with minimal distances can be achieved. ECGA employs the least complicated and the most closely related probabilistic model structure regarding population space during generation cycle. This feature strengthens the comprehension of clustering results in how such optimal solutions were achieved. The proposed method was tested on real-world time series data, open to the public, from manufacturing industry, and showed noticeable performances compared to well-established methods. Accordingly, this paper demonstrates that obtaining both comprehensible result as well as prominent performance is feasible by employing metaheuristic techniques to time series data clustering methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
36. Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey.
- Author
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Zehra, Sehar, Faseeha, Ummay, Syed, Hassan Jamil, Samad, Fahad, Ibrahim, Ashraf Osman, Abulfaraj, Anas W., and Nagmeldin, Wamda
- Subjects
ANOMALY detection (Computer security) ,CYBERTERRORISM ,SENSOR networks ,COST control ,MACHINERY - Abstract
Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
37. UAV Abnormal State Detection Model Based on Timestamp Slice and Multi-Separable CNN.
- Author
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Yang, Tao, Chen, Jiangchuan, Deng, Hongli, and Lu, Yu
- Subjects
CONVOLUTIONAL neural networks ,DEEP learning ,MACHINE learning ,IMAGE reconstruction - Abstract
With the rapid development of UAVs (Unmanned Aerial Vehicles), abnormal state detection has become a critical technology to ensure the flight safety of UAVs. The position and orientation system (POS) data, etc., used to evaluate UAV flight status are from different sensors. The traditional abnormal state detection model ignores the difference of POS data in the frequency domain during feature learning, which leads to the loss of key feature information and limits the further improvement of detection performance. To deal with this and improve UAV flight safety, this paper presents a method for detecting the abnormal state of a UAV based on a timestamp slice and multi-separable convolutional neural network (TS-MSCNN). Firstly, TS-MSCNN divides the POS data reasonably in the time domain by setting a set of specific timestamps and then extracts and fuses the key features to avoid the loss of feature information. Secondly, TS-MSCNN converts these feature data into grayscale images by data reconstruction. Lastly, TS-MSCNN utilizes a multi-separable convolution neural network (MSCNN) to learn key features more effectively. The binary and multi-classification experiments conducted on the real flight data, Air Lab Fault and Anomaly (ALFA), demonstrate that the TS-MSCNN outperforms traditional machine learning (ML) and the latest deep learning methods in terms of accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
38. Correlation-Based Anomaly Detection in Industrial Control Systems.
- Author
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Jadidi, Zahra, Pal, Shantanu, Hussain, Mukhtar, and Nguyen Thanh, Kien
- Subjects
INDUSTRIAL controls manufacturing ,ANOMALY detection (Computer security) ,RECURRENT neural networks ,INTRUSION detection systems (Computer security) ,CYBERTERRORISM ,INFORMATION technology ,STATISTICAL correlation - Abstract
Industrial Control Systems (ICSs) were initially designed to be operated in an isolated network. However, recently, ICSs have been increasingly connected to the Internet to expand their capability, such as remote management. This interconnectivity of ICSs exposes them to cyber-attacks. At the same time, cyber-attacks in ICS networks are different compared to traditional Information Technology (IT) networks. Cyber attacks on ICSs usually involve a sequence of actions and a multitude of devices. However, current anomaly detection systems only focus on local analysis, which misses the correlation between devices and the progress of attacks over time. As a consequence, they lack an effective way to detect attacks at an entire network scale and predict possible future actions of an attack, which is of significant interest to security analysts to identify the weaknesses of their network and prevent similar attacks in the future. To address these two key issues, this paper presents a system-wide anomaly detection solution using recurrent neural networks combined with correlation analysis techniques. The proposed solution has a two-layer analysis. The first layer targets attack detection, and the second layer analyses the detected attack to predict the next possible attack actions. The main contribution of this paper is the proof of the concept implementation using two real-world ICS datasets, SWaT and Power System Attack. Moreover, we show that the proposed solution effectively detects anomalies and attacks on the scale of the entire ICS network. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
39. SSMSPC: self-supervised multivariate statistical in-process control in discrete manufacturing processes
- Author
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Biegel, Tobias, Helm, Patrick, Jourdan, Nicolas, and Metternich, Joachim
- Published
- 2024
- Full Text
- View/download PDF
40. Enhancing anomaly detectors with LatentOut
- Author
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Angiulli, Fabrizio, Fassetti, Fabio, and Ferragina, Luca
- Published
- 2024
- Full Text
- View/download PDF
41. Low-cost and high-performance abnormal trajectory detection based on the GRU model with deep spatiotemporal sequence analysis in cloud computing.
- Author
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Tang, Guohao, Zhao, Huaying, and Yu, Baohua
- Subjects
SEQUENCE analysis ,RANDOM forest algorithms ,RECURRENT neural networks ,ANOMALY detection (Computer security) ,CLOUD computing ,PROCESS capability ,SEMANTICS - Abstract
Trajectory anomalies serve as early indicators of potential issues and frequently provide valuable insights into event occurrence. Existing methods for detecting abnormal trajectories primarily focus on comparing the spatial characteristics of the trajectories. However, they fail to capture the temporal dimension's pattern and evolution within the trajectory data, thereby inadequately identifying the behavioral inertia of the target group. A few detection methods that incorporate spatiotemporal features have also failed to adequately analyze the spatiotemporal sequence evolution information; consequently, detection methods that ignore temporal and spatial correlations are too one-sided. Recurrent neural networks (RNNs), especially gate recurrent unit (GRU) that design reset and update gate control units, process nonlinear sequence processing capabilities, enabling effective extraction and analysis of both temporal and spatial characteristics. However, the basic GRU network model has limited expressive power and may not be able to adequately capture complex sequence patterns and semantic information. To address the above issues, an abnormal trajectory detection method based on the improved GRU model is proposed in cloud computing in this paper. To enhance the anomaly detection ability and training efficiency of relevant models, strictly control the input of irrelevant features and improve the model fitting effect, an improved model combining the random forest algorithm and fully connected layer network is designed. The method deconstructs spatiotemporal semantics through reset and update gated units, while effectively capturing feature evolution information and target behavioral inertia by leveraging the integration of features and nonlinear mapping capabilities of the fully connected layer network. The experimental results based on the GeoLife GPS trajectory dataset indicate that the proposed approach improves both generalization ability by 1% and reduces training cost by 31.68%. This success do provides a practical solution for the task of anomaly trajectory detection. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
42. 基于 SSA−LSTM 的风速异常波动检测方法.
- Author
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邓立军, 袁金波, 刘剑, and 尚文天
- Abstract
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- Published
- 2024
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43. Intelligent systems for sitting posture monitoring and anomaly detection: an overview.
- Author
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Vermander, Patrick, Mancisidor, Aitziber, Cabanes, Itziar, and Perez, Nerea
- Subjects
ARTIFICIAL intelligence ,MEDICAL personnel ,POSTURE ,ELECTRIC wheelchairs ,FUNCTIONAL status ,RESEARCH personnel ,YOGA postures ,INTRUSION detection systems (Computer security) - Abstract
The number of people who need to use wheelchair for proper mobility is increasing. The integration of technology into these devices enables the simultaneous and objective assessment of posture, while also facilitating the concurrent monitoring of the functional status of wheelchair users. In this way, both the health personnel and the user can be provided with relevant information for the recovery process. This information can be used to carry out an early adaptation of the rehabilitation of patients, thus allowing to prevent further musculoskeletal problems, as well as risk situations such as ulcers or falls. Thus, a higher quality of life is promoted in affected individuals. As a result, this paper presents an orderly and organized analysis of the existing postural diagnosis systems for detecting sitting anomalies in the literature. This analysis can be divided into two parts that compose such postural diagnosis: on the one hand, the monitoring devices necessary for the collection of postural data and, on the other hand, the techniques used for anomaly detection. These anomaly detection techniques will be explained under two different approaches: the traditional generalized approach followed to date by most works, where anomalies are treated as incorrect postures, and a new individualized approach treating anomalies as changes with respect to the normal sitting pattern. In this way, the advantages, limitations and opportunities of the different techniques are analyzed. The main contribution of this overview paper is to synthesize and organize information, identify trends, and provide a comprehensive understanding of sitting posture diagnosis systems, offering researchers an accessible resource for navigating the current state of knowledge of this particular field. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
44. Efficient Defect Detection of Rotating Goods under the Background of Intelligent Retail.
- Author
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Hu, Zhengming, Zeng, Xuepeng, Xie, Kai, Wen, Chang, He, Jianbiao, and Zhang, Wei
- Subjects
VENDING machines ,PRODUCT returns ,CONSUMERS - Abstract
Dynamic visual vending machines are rapidly growing in popularity, offering convenience and speed to customers. However, there is a prevalent issue with consumers damaging goods and then returning them to the machine, severely affecting business interests. This paper addresses the issue from the standpoint of defect detection. Although existing industrial defect detection algorithms, such as PatchCore, perform well, they face challenges, including handling goods in various orientations, detection speeds that do not meet real-time monitoring requirements, and complex backgrounds that hinder detection accuracy. These challenges hinder their application in dynamic vending environments. It is crucial to note that efficient visual features play a vital role in memory banks, yet current memory repositories for industrial inspection algorithms do not adequately address the problem of location-specific feature redundancy. To tackle these issues, this paper introduces a novel defect detection algorithm for goods using adaptive subsampling and partitioned memory banks. Firstly, Grad-CAM is utilized to extract deep features, which, in combination with shallow features, mitigate the impact of complex backgrounds on detection accuracy. Next, graph convolutional networks extract rotationally invariant features. The adaptive subsampling partitioned memory bank is then employed to store features of non-defective goods, which reduces memory consumption and enhances training speed. Experimental results on the MVTec AD dataset demonstrate that the proposed algorithm achieves a marked improvement in detection speed while maintaining accuracy that is comparable to state-of-the-art models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. From Anomaly Detection to Defect Classification.
- Author
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Klarák, Jaromír, Andok, Robert, Malík, Peter, Kuric, Ivan, Ritomský, Mário, Klačková, Ivana, and Tsai, Hung-Yin
- Subjects
INTRUSION detection systems (Computer security) ,SYSTEMS design ,CLASSIFICATION ,PROOF of concept ,DEEP learning ,INSPECTION & review - Abstract
This paper proposes a new approach to defect detection system design focused on exact damaged areas demonstrated through visual data containing gear wheel images. The main advantage of the system is the capability to detect a wide range of patterns of defects occurring in datasets. The methodology is built on three processes that combine different approaches from unsupervised and supervised methods. The first step is a search for anomalies, which is performed by defining the correct areas on the controlled object by using the autoencoder approach. As a result, the differences between the original and autoencoder-generated images are obtained. These are divided into clusters using the clustering method (DBSCAN). Based on the clusters, the regions of interest are subsequently defined and classified using the pre-trained Xception network classifier. The main result is a system capable of focusing on exact defect areas using the sequence of unsupervised learning (autoencoder)–unsupervised learning (clustering)–supervised learning (classification) methods (U2S-CNN). The outcome with tested samples was 177 detected regions and 205 occurring damaged areas. There were 108 regions detected correctly, and 69 regions were labeled incorrectly. This paper describes a proof of concept for defect detection by highlighting exact defect areas. It can be thus an alternative to using detectors such as YOLO methods, reconstructors, autoencoders, transformers, etc. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. Dynamic Data Abstraction-Based Anomaly Detection for Industrial Control Systems.
- Author
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Cho, Jake and Gong, Seonghyeon
- Subjects
INTRUSION detection systems (Computer security) ,ANOMALY detection (Computer security) ,INDUSTRIAL controls manufacturing ,INFORMATION technology ,MACHINE learning ,NOISE control - Abstract
Industrial control systems (ICS) are critical networks directly linked to the value of core national and societal assets, yet they are increasingly becoming primary targets for numerous cyberattacks today. The ICS network, a fusion of operational technology (OT) and information technology (IT) networks, possesses a broad attack vector, and attacks targeting ICS often take the form of advanced persistent threats (APTs) exploiting zero-day vulnerabilities. However, most existing ICS security techniques have been adaptations of security technologies for IT networks, and security measures tailored to the characteristics of ICS data are currently insufficient. To mitigate cyber threats to ICS networks, this paper proposes an anomaly detection technique based on dynamic data abstraction. The proposed method abstracts ICS data collected in real time using a dynamic data abstraction technique based on noise reduction. The abstracted data are then used to optimize both the update rate and the detection accuracy of the anomaly detection model through model adaptation and incremental learning processes. The proposed approach updates the model by quickly reflecting data on new attack patterns and their distributions, effectively shortening the dwell time in response to APTs utilizing zero-day vulnerabilities. We demonstrate the attack response performance and detection accuracy of the proposed dynamic data abstraction-based anomaly detection technique through experiments using the SWaT dataset generated from a testbed of an actual ICS process. The experiments show that the proposed model achieves high accuracy with a small number of abstracted data while rapidly learning new attack pattern data in real-time without compromising accuracy. The proposed technique can effectively respond to cyberattacks targeting ICS by quickly learning and reflecting trends in attack patterns that exploit zero-day vulnerabilities. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
47. Two-Stage Ultrasound Signal Recognition Method Based on Envelope and Local Similarity Features.
- Author
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Wang, Liwei, Lu, Senxiang, Liu, Xiaoyuan, and Liu, Jinhai
- Subjects
DEEP learning ,SIGNAL classification ,FEATURE extraction ,ULTRASONIC imaging ,SIGNAL detection ,RECOGNITION (Psychology) ,MACHINE learning ,CONTRAST-enhanced ultrasound - Abstract
Accurate identification of ultrasonic signals can effectively improve the accuracy of a defect detection and inversion. Current methods, based on machine learning and deep learning have been able to classify signals with significant differences. However, the ultrasonic internal detection signal is interspersed with a large number of anomalous signals of an unknown origin and is affected by the time shift of echo features and noise interference, which leads to the low recognition accuracy of the ultrasonic internal detection signal, at this stage. To address the above problems, this paper proposes a two-stage ultrasonic signal recognition method, based on the envelope and local similarity features (TS-ES). In the first stage, a normal signal classification method, based on the envelope feature extraction and fusion is proposed to solve the problem of the low ultrasonic signal classification accuracy under the conditions of the echo feature time shift and noise interference. In the second stage, an abnormal signal detection method, based on the local similarity feature extraction and enhancement is proposed to solve the problem of detecting abnormal signals in ultrasound internal detection data. The experimental results show that the accuracy of the two-stage ultrasonic signal recognition method, based on the envelope and local similarity features (TS-ES) in this paper is 97.43%, and the abnormal signal detection accuracy and recall rate are as high as 99.7% and 97.81%. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
48. Ship Classification and Anomaly Detection Based on Spaceborne AIS Data Considering Behavior Characteristics.
- Author
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Yan, Zhenguo, Song, Xin, Zhong, Hanyang, Yang, Lei, and Wang, Yitao
- Subjects
ANOMALY detection (Computer security) ,SHIPBORNE automatic identification systems ,IMMUNOCOMPUTERS - Abstract
With the establishment of satellite constellations equipped with ship automatic identification system (AIS) receivers, the amount of AIS data is continuously increasing, and AIS data have become an important part of ocean big data. To further improve the ability to use AIS data for maritime surveillance, it is necessary to explore a ship classification and anomaly detection method suitable for spaceborne AIS data. Therefore, this paper proposes a ship classification and anomaly detection method based on machine learning that considers ship behavior characteristics for spaceborne AIS data. In view of the characteristics of different types of ships, this paper introduces the extraction and analysis of ship behavior characteristics in addition to traditional geometric features and discusses the ability of the proposed method for ship classification and anomaly detection. The experimental results show that the classification accuracy of the five types of ships can reach 92.70%, and the system can achieve better results in the other classification evaluation metrics by considering the ship behavior characteristics. In addition, this method can accurately detect anomalous ships, which further proves the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
49. Multivariate time series anomaly detection via dynamic graph attention network and Informer.
- Author
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Huang, Xiangheng, Chen, Ningjiang, Deng, Ziyue, and Huang, Suqun
- Subjects
ANOMALY detection (Computer security) ,TIME series analysis ,FALSE alarms ,COMPUTER software quality control ,TIMESTAMPS - Abstract
In the industrial Internet, industrial software plays a central role in enhancing the level of intelligent manufacturing. It enables the promotion of digital collaborative services. Effective anomaly detection of multivariate time series can ensure the quality of industrial software. Extensive research has been conducted on time series anomaly detection to identify abnormal data. However, detecting anomalies in multivariate time series, which consist of high-dimensional, high-noise, and random data, poses significant challenges. The states of different timestamps within a time series sample can influence the overall correlation of sensor features. Unfortunately, existing methods often overlook this impact, making it difficult to capture subtle variations in the delayed response of attacked sensors.Consequently, there are false alarms and abnormal omissions. To address these limitations, this paper proposes an anomaly detection method called DGINet. DGINet leverages a dynamic graph attention network and Informer to capture and integrate feature correlation across different time states. By combining GRU and Informer, DGINet effectively captures continuous correlations in long time series. Moreover, DGINet simultaneously optimizes the reconstruction and forecasting modules, enhancing its overall performance. Experimental results on four benchmark datasets demonstrate that DGINet outperforms state-of-the-art methods by achieving up to a 2 % improvement in accuracy. Further analysis reveals that DGINet excels in accurately detecting anomalies in long time series and locating candidate abnormal attack points. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance.
- Author
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Wu, Bin and Wang, Xiaoqi
- Abstract
Industrial anomaly detection is constrained by the scarcity of anomaly samples, limiting the applicability of supervised learning methods. Many studies have focused on anomaly detection by generating anomaly images and adopting self-supervised learning approaches. Leveraging pre-trained networks on ImageNet has been explored to assist in this training process. However, achieving accurate anomaly detection remains time-consuming due to the network's depth and parameter count not being reduced. In this paper, we propose a self-supervised learning method based on Feature Enhancement Patch Distribution Modeling (FEPDM), which generates simulated anomalies. Unlike direct training on the original feature extraction network, our approach utilizes a pre-trained network to extract multi-scale features. By aggregating these multi-scale features, we are able to train at the feature level, thereby adapting more efficiently to various network structures and reducing domain bias with respect to natural image classification. Additionally, it significantly reduces the number of parameters in the training process. Introducing this approach not only enhances the model's generalization ability but also significantly improves the efficiency of anomaly detection. The method was evaluated on MVTec AD and BTAD datasets, and (image-level, pixel-level) AUROC scores of (95.7%, 96.2%), (93.4%, 97.6%) were obtained, respectively. The experimental results have convincingly demonstrated the efficacy of our method in tackling the scarcity of abnormal samples in industrial scenarios, while simultaneously highlighting its broad generalizability. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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