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41,493 results on '"Anomaly Detection"'

1. Autoencoder-based detector for distinguishing process anomaly and sensor failure.

Author

Lee, Chia-Yen, Chang, Kai, and Ho, Chien

Subjects
DEEP learning, FALSE alarms, MANUFACTURING processes, HETEROSCEDASTICITY, DETECTORS
Abstract

Anomaly detection is a frequently discussed topic in manufacturing. However, the issues of anomaly detection are typically attributed to the manufacturing process or equipment itself. In practice, the sensor responsible for collecting data and monitoring values may fail, leading to a biased detection result – false alarm. In such cases, replacing the sensor is necessary instead of performing equipment maintenance. This study proposes an effective framework embedded with autoencoder-based control limits that can dynamically distinguish sensor anomaly from process anomaly in real-time. We conduct a simulation numerical study and an empirical study of semiconductor assembling manufacturers to validate the proposed framework. The results show that the proposed model outperforms other benchmark methods and can successfully identify sensor failures, even under conditions of (1) large variations in process values or sensor values and (2) heteroscedasticity effect. This is particularly beneficial in various practical applications where sensors are used for numerical measurements and support equipment maintenance. [ABSTRACT FROM AUTHOR]

Published
2024
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2. Variational AutoEncoders-LSTM based fault detection of time-dependent high dimensional processes.

Author

Maged, Ahmed, Lui, Chun Fai, Haridy, Salah, and Xie, Min

Subjects
DEEP learning, QUALITY control charts, MANUFACTURING processes, FALSE alarms, INDUSTRIAL costs, INTELLIGENT tutoring systems
Abstract

In modern large-scale industrial processes, data are often high dimensional time-dependent due to the frequent sampling, dynamic nature and large number of variables. Appropriate monitoring of such processes allows for efficient decision-making that can improve the baseline of manufacturing companies either through decreasing production costs or enhancing production efficiency. Various latent variable-based control charts have been proposed for addressing high dimensional data; however, many of these methods assume that the data are independent and normally distributed. The violation of these assumptions results in an increased false alarm rate, in addition to the deterioration in the performance of such methods. In this study, we propose a Variational Autoencoder-Long Short Term Memory (VAE-LSTM) deep learning based $ {T^2} $ T 2 chart that integrates the unique features of both VAE and LSTM for intelligent fault detection of time-dependent high dimensional processes. The effectiveness and applicability of the proposed model are demonstrated through extensive simulations, an open-source online dataset, and a real case study. [ABSTRACT FROM AUTHOR]

Published
2024
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3. Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis

Author

Sun, Yibin, Gomes, Heitor Murilo, Pfahringer, Bernhard, Bifet, Albert, Goos, Gerhard, Series 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, Hadfi, Rafik, editor, Anthony, Patricia, editor, Sharma, Alok, editor, Ito, Takayuki, editor, and Bai, Quan, editor

Published
2025
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8. Meta-TadGAN: Time Series Anomaly Detection Using TadGAN with Meta-features

Author

Silva, Inês Oliveira e, Soares, Carlos, Cerqueira, Vitor, Rodrigues, Arlete, Bastardo, Pedro, Goos, Gerhard, Series 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, Santos, Manuel Filipe, editor, Machado, José, editor, Novais, Paulo, editor, Cortez, Paulo, editor, and Moreira, Pedro Miguel, editor

Published
2025
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9. Detection and Localization of Malicious Nodes in Internet of Things Based on SDN

Author

Jingxu, Xiao, Chaowen, Chang, Lu, Yuan, Yingying, Ma, Chenli, Yang, Goos, Gerhard, Series 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, Cai, Zhipeng, editor, Takabi, Daniel, editor, Guo, Shaoyong, editor, and Zou, Yifei, editor

Published
2025
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10. Anomaly Detection Under Normality-Shifted IoT Scenario: Filter, Detection, and Adaption

Author

Pan, Mengying, Tang, Wenyi, He, Zaobo, Chen, Bingyu, Goos, Gerhard, Series 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, Cai, Zhipeng, editor, Takabi, Daniel, editor, Guo, Shaoyong, editor, and Zou, Yifei, editor

Published
2025
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11. Improving Anomaly Scene Recognition with Large Vision-Language Models

Author

Liu, Cheng, Long, Xianlei, Li, Yan, Chen, Chao, Gu, Fuqiang, Yuan, Songyu, Zhang, Chunlong, Goos, Gerhard, Series 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, Cai, Zhipeng, editor, Takabi, Daniel, editor, Guo, Shaoyong, editor, and Zou, Yifei, editor

Published
2025
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12. Variational Autoencoder Based Automatic Clustering for Multivariate Time Series Anomaly Detection

Author

Yan, Li, Hu, Hailin, Yang, Kun, Wang, Gaozhou, Guan, Ti, Zheng, Yanwei, Goos, Gerhard, Series 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, Cai, Zhipeng, editor, Takabi, Daniel, editor, Guo, Shaoyong, editor, and Zou, Yifei, editor

Published
2025
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13. Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts

Author

Hou, Yanning, Xu, Ke, Li, Junfa, Ruan, Yanran, Qiu, Jianfeng, Goos, Gerhard, Series 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, Lin, Zhouchen, editor, Cheng, Ming-Ming, editor, He, Ran, editor, Ubul, Kurban, editor, Silamu, Wushouer, editor, Zha, Hongbin, editor, Zhou, Jie, editor, and Liu, Cheng-Lin, editor

Published
2025
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14. Continuous Memory Representation for Anomaly Detection

Author

Lee, Joo Chan, Kim, Taejune, Park, Eunbyung, Woo, Simon S., Ko, Jong Hwan, Goos, Gerhard, Series 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, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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15. Intrusion Detection at the IoT Edge Using Federated Learning

Author

Pope, James, Spyridopoulos, Theodoros, Kumar, Vijay, Raimondo, Francesco, Gunner, Sam, Oikonomou, George, Pasquier, Thomas, McConville, Ryan, Carnelli, Pietro, Sanchez-Mompo, Adrian, Mavromatis, Ioannis, Khan, Aftab, Goos, Gerhard, Series 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, Pitropakis, Nikolaos, editor, and Katsikas, Sokratis, editor

Published
2025
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16. Learning Diffusion Models for Multi-view Anomaly Detection

Author

Liu, Chieh, Chu, Yu-Min, Hsieh, Ting-I, Chen, Hwann-Tzong, Liu, Tyng-Luh, Goos, Gerhard, Series 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, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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17. Impact of the Neighborhood Parameter on Outlier Detection Algorithms

Author

Iglesias, Félix, Martínez, Conrado, Zseby, Tanja, Goos, Gerhard, Series 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, Chávez, Edgar, editor, Kimia, Benjamin, editor, Lokoč, Jakub, editor, Patella, Marco, editor, and Sedmidubsky, Jan, editor

Published
2025
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18. Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers

Author

Röchner, Philipp, Marques, Henrique O., Campello, Ricardo J. G. B., Zimek, Arthur, Rothlauf, Franz, Goos, Gerhard, Series 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, Chávez, Edgar, editor, Kimia, Benjamin, editor, Lokoč, Jakub, editor, Patella, Marco, editor, and Sedmidubsky, Jan, editor

Published
2025
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19. A Framework Based on Internet of Things and Recommender Systems for Thermal Facilities

Author

Colace, Francesco, Gaeta, Rosario, Santaniello, Domenico, Troiano, Alfredo, Valentino, Carmine, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Yang, Xin-She, editor, Sherratt, R. Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor

Published
2025
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20. Anomaly Detection Within Mission-Critical Call Processing

Author

Doris, Sean, Salem, Iosif, Schmid, Stefan, Goos, Gerhard, Series 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, Masuzawa, Toshimitsu, editor, Katayama, Yoshiaki, editor, Kakugawa, Hirotsugu, editor, Nakamura, Junya, editor, and Kim, Yonghwan, editor

Published
2025
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21. Face Anti-spoofing Based on Multi-view Anomaly Detection

Author

Zheng, Yu, Wang, Jiahui, Jing, Jiuyao, Peng, Chunlei, Goos, Gerhard, Series 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, Lin, Zhouchen, editor, Cheng, Ming-Ming, editor, He, Ran, editor, Ubul, Kurban, editor, Silamu, Wushouer, editor, Zha, Hongbin, editor, Zhou, Jie, editor, and Liu, Cheng-Lin, editor

Published
2025
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22. Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection

Author

Tu, Yuanpeng, Zhang, Boshen, Liu, Liang, Li, Yuxi, Zhang, Jiangning, Wang, Yabiao, Wang, Chengjie, Zhao, Cairong, Goos, Gerhard, Series 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, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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26. Blockchain-Based Anomaly Detection and Intrusion Prevention in IoT Networks

Author

Ganesh Kumar, G., Kanakaprabha, S., Venu Gopal, Gaddam, Shaik, Riaz, Senthil Kumar, T., Udhaya Kumar, T., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Lin, Frank, editor, Pastor, David, editor, Kesswani, Nishtha, editor, Patel, Ashok, editor, Bordoloi, Sushanta, editor, and Koley, Chaitali, editor

Published
2025
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29. Semi-supervised Three-Dimensional Detection of Congenital Brain Anomalies in First Trimester Ultrasound

Author

Zijta, Marcella C., Bastiaansen, Wietske A. P., Wijnen, Rene M. H., Steegers-Theunissen, Régine P. M., de Bakker, Bernadette S., Rousian, Melek, Klein, Stefan, Goos, Gerhard, Series 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, Link-Sourani, Daphna, editor, Abaci Turk, Esra, editor, Macgowan, Christopher, editor, Hutter, Jana, editor, Melbourne, Andrew, editor, and Licandro, Roxane, editor

Published
2025
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30. Early Failure Detection for Air Production Unit in Metro Trains

Author

Zafra, Amelia, Veloso, Bruno, Gama, João, Goos, Gerhard, Series 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, Quintián, Héctor, editor, Corchado, Emilio, editor, Troncoso Lora, Alicia, editor, Pérez García, Hilde, editor, Jove Pérez, Esteban, editor, Calvo Rolle, José Luis, editor, Martínez de Pisón, Francisco Javier, editor, García Bringas, Pablo, editor, Martínez Álvarez, Francisco, editor, Herrero, Álvaro, editor, and Fosci, Paolo, editor

Published
2025
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31. Development of a Machine Learning Model for Enhancing the Security of the Internet of Things (IoT) System

Author

Raminenei, Kamalakar, Gupta, Vratika, Durgam, Thirupathi, Kapila, Dhiraj, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Kumar, Amit, editor, Gunjan, Vinit Kumar, editor, Senatore, Sabrina, editor, and Hu, Yu-Chen, editor

Published
2025
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32. LoRIS - Weakly-Supervised Anomaly Detection for Ultrasound Images

Author

Colussi, Marco, Ahmetovic, Dragan, Civitarese, Gabriele, Bettini, Claudio, Solyman, Aiman, Gualtierotti, Roberta, Peyvandi, Flora, Mascetti, Sergio, Goos, Gerhard, Series 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, Gomez, Alberto, editor, Khanal, Bishesh, editor, King, Andrew, editor, and Namburete, Ana, editor

Published
2025
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33. Unsupervised Detection of Fetal Brain Anomalies Using Denoising Diffusion Models

Author

Olsen, Markus Ditlev Sjøgren, Ambsdorf, Jakob, Lin, Manxi, Taksøe-Vester, Caroline, Svendsen, Morten Bo Søndergaard, Christensen, Anders Nymark, Nielsen, Mads, Tolsgaard, Martin Grønnebæk, Feragen, Aasa, Pegios, Paraskevas, Goos, Gerhard, Series 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, Gomez, Alberto, editor, Khanal, Bishesh, editor, King, Andrew, editor, and Namburete, Ana, editor

Published
2025
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34. Beyond Heatmaps: A Comparative Analysis of Metrics for Anomaly Localization in Medical Images

Author

Zimmerer, David, Maier-Hein, Klaus, Goos, Gerhard, Series 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, Sudre, Carole H., editor, Mehta, Raghav, editor, Ouyang, Cheng, editor, Qin, Chen, editor, Rakic, Marianne, editor, and Wells, William M., editor

Published
2025
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35. Image-Conditioned Diffusion Models for Medical Anomaly Detection

Author

Baugh, Matthew, Reynaud, Hadrien, Marimont, Sergio Naval, Cechnicka, Sarah, Müller, Johanna P., Tarroni, Giacomo, Kainz, Bernhard, Goos, Gerhard, Series 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, Sudre, Carole H., editor, Mehta, Raghav, editor, Ouyang, Cheng, editor, Qin, Chen, editor, Rakic, Marianne, editor, and Wells, William M., editor

Published
2025
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36. Active Learning for Scribble-Based Diffusion MRI Segmentation

Author

Lennartz, Jonathan, Pohl, Golo, Schultz, Thomas, Goos, Gerhard, Series 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, Sudre, Carole H., editor, Mehta, Raghav, editor, Ouyang, Cheng, editor, Qin, Chen, editor, Rakic, Marianne, editor, and Wells, William M., editor

Published
2025
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39. AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection

Author

Cao, Yunkang, Zhang, Jiangning, Frittoli, Luca, Cheng, Yuqi, Shen, Weiming, Boracchi, Giacomo, Goos, Gerhard, Series 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, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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40. TransFusion – A Transparency-Based Diffusion Model for Anomaly Detection

Author

Fučka, Matic, Zavrtanik, Vitjan, Skočaj, Danijel, Goos, Gerhard, Series 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, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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45. The Shewhart-type RZ control chart for monitoring the ratio of autocorrelated variables.

Author

Nguyen, Huu Du, Nadi, Adel Ahmadi, Tran, Kim Duc, Castagliola, Philippe, Celano, Giovanni, and Tran, Kim Phuc

Subjects
QUALITY control charts, VECTOR autoregression model, AUTOREGRESSIVE models, MANUFACTURING processes, DIRECT-fired heaters, TIME series analysis
Abstract

In many industrial manufacturing processes, the quality of products can depend on the relative amount between two quality characteristics X and Y. Often, this calls for the on-line monitoring of the ratio Z = X / Y as a quality characteristic itself by means of a control chart. A large number of control charts monitoring the ratio have been investigated in the literature under the assumption of independent normal observations of the two quality characteristics. In practice, due to the high frequency in sensor data collection, both autocorrelation and cross-correlation between consecutive observations can exist for X and Y and should be modelled to protect against the false alarm rate inflation when implementing a control chart for monitoring the ratio Z = X / Y. In this paper, we tackle this problem by investigating the performance of the Phase II Shewhart-type RZ control chart monitoring the ratio of two normal variables whose relationship is captured by a bivariate time series autoregressive model VAR(1), which can also account for the cross-correlation between the two quality characteristics. With the numerical study, we discuss how the design and the statistical performance of the Shewhart-type RZ control chart change with the VAR(1) model's parameters. We also provide an example to illustrate the use of the Shewhart-type RZ control chart with bivariate time series of observations in a furnace process. [ABSTRACT FROM AUTHOR]

Published
2023
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47. FRC‐SGAN based anomaly event recognition for computer night vision in edge and cloud environment.

Author

V, Charles Prabu and Perumal, Pandiaraja

Subjects
CONVOLUTIONAL neural networks, GENERATIVE adversarial networks, FEATURE extraction, IMAGE recognition (Computer vision), COMPUTER vision
Abstract

Summary: Anomaly event recognition and identification has a crucial part in several areas, particularly in night vision environments. Conventional techniques of event recognition are hugely based upon data extracted from certain images for classification purposes. This needs users to select suitable features to establish the feature depictions for actual images per definite situations. Manual feature selection is laborious as well as heuristic tasks and the features obtained in this manner generally have worse robustness. Here, a Faster Region‐based Convolutional fused Social Generative Adversarial Network (FRC‐SGAN) is designed for anomaly event recognition in a night vision environment. At the cloud, key frame extraction, pre‐processing, feature extraction, human detection (HD) and anomalous event recognition are carried out. Initially, input video from the database is subjected to perform pre‐processing. The visibility enhancement is utilized for pre‐processing. Thereafter, features like ResNet features, texture features and statistical features are extracted. Then, HD is accomplished by DeepJoint segmentation with chord distance. Finally, anomalous detection is done by FRC‐SGAN that is the incorporation of Fast Regional Convolutional Neural Network (FR‐CNN) and Social Generative Adversarial Network (SGAN). In addition, FRC‐SGAN acquired 90.8% of accuracy, 89.7% of precision, and 89.2% of recall. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Detection of Incidents and Anomalies in Software-Defined Network -- Based Implementations of Critical Infrastructure Resulting in Adaptive System Changes.

Author

Organiściak, Patryk, Kuraś, Paweł, Strzalka, Dominik, Paszkiewicz, Andrzej, Bolanowski, Marek, Kowal, Bartosz, Ćmil, Michał, Dymora, Paweł, Mazurek, Mirosław, and Vanivska, Veronika

Subjects
OPENFLOW (Computer network protocol), INFRASTRUCTURE (Economics), ANOMALY detection (Computer security), SOFTWARE-defined networking, TRAFFIC engineering
Abstract

In the paper an example of an integrated software-defined network (SDN) system with heterogeneous technological instances based on the Linux platform will be shown. For this purpose, two research testing stands with a POX controller and OVS (Open vSwitch) switches were used. In the first testing stand, the research based on the ICMP traffic was done while in the second one, MQTT traffic was analysed. The capabilities of these systems were examined in terms of responding to detected incidents and traffic anomalies. In particular, their appropriate responses to anomalies were tested, as well as the possibility of continuous monitoring of packet transfer between separate network components. The aim of the paper is to investigate the effectiveness of SDN in enhancing the security and adaptability of critical infrastructure systems. For isolation and optimised resource management, some components, such as POX or the MQTT broker, were run in Docker containers. The test environment used both hardware cases and prepared software, enabling comprehensive design and testing of networks based on the OpenFlow protocol used in SDN architecture, enabling the separation of control from traffic in computer networks. The results of this research make it possible to implement anomaly detection solutions in critical infrastructure systems that will adapt on the fly to changing conditions that arise, for example, in the case of an attack on such infrastructure or physical damage to it at a selected node. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Building an intrusion detection system on UNSW‐NB15: Reducing the margin of error to deal with data overlap and imbalance.

Author

Zoghi, Zeinab and Serpen, Gursel

Subjects
ANOMALY detection (Computer security), COMPUTER network security, RANDOM forest algorithms, SECURITY classification (Government documents), MACHINE learning, INTRUSION detection systems (Computer security)
Abstract

Summary: This study addresses the challenge of data imbalance and class overlap in machine learning for intrusion detection, proposing that targeted algorithmic adjustments can significantly enhance model performance. Our hypothesis contends that an ensemble framework, adeptly integrating novel threshold‐adjustment algorithms, can improve classification sensitivity and specificity. To test this, we developed an ensemble model comprising Balanced Bagging (BB), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF), fine‐tuned using grid search for BB and XGBoost, and augmented with the Hellinger metric for RF to tackle data imbalance. The innovation lies in our algorithms, which adeptly adjust the discrimination threshold to rectify the class overlap problem, enhancing the model's ability to discern between negative and positive classes. Utilizing the UNSW‐NB15 dataset, we conducted a comparative analysis for binary and multi‐category classification. Our ensemble model achieved a binary classification accuracy of 97.80%, with a sensitivity rate of 98.26% for detecting attacks, and a multi‐category classification accuracy and sensitivity that reached up to 99.73% and 97.24% for certain attack types. These results substantially surpass those of existing models on the same dataset, affirming our model's superiority in dealing with complex data distributions prevalent in network security domains. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing.

Author

Li, Yuxuan, Xie, Tianxin, Liu, Chenang, and Shi, Zhangyue

Abstract

AbstractThe incorporation of advanced sensors and machine learning techniques has enabled modern manufacturing enterprises to perform data-driven classification-based anomaly detection based on the sensor data collected in manufacturing processes. However, one critical challenge is that newly presented defect category may manifest as the manufacturing process continues, resulting in monitoring performance deterioration of previously trained machine learning models. Hence, there is an increasing need for empowering machine learning models to learn continually. Among all continual learning methods, memory-based continual learning has the best performance but faces the constraints of data storage capacity. To address this issue, this paper develops a novel pseudo replay-based continual learning framework by integrating class incremental learning and oversampling-based data generation. Without storing all the data, the developed framework could generate high-quality data representing previous classes to train machine learning model incrementally when new category anomaly occurs. In addition, it could even enhance the monitoring performance since it also effectively improves the data quality. The effectiveness of the proposed framework is validated in three cases studies, which leverages supervised classification problem for anomaly detection. The experimental results show that the developed method is very promising in detecting novel anomaly while maintaining a good performance on the previous task and brings up more flexibility in model architecture. [ABSTRACT FROM AUTHOR]

Published
2024
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