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DDoS attacks remain a persistent cybersecurity threat, blocking services to legitimate users and causing significant damage to reputation, finances, and potential customers. For the detection of DDoS attacks, machine learning techniques such as supervised learning have been extensively employed, but their effectiveness declines when the framework confronts patterns exterior to the dataset. In addition, DDoS attack schemes continue to improve, rendering conventional data model-based training ineffectual. We have developed a novelty open-set recognition framework for DDoS attack detection to overcome the challenges of traditional methods. Our framework is built on a Convolutional Neural Network (CNN) construction featuring geometrical metric (CNN-Geo), which utilizes deep learning techniques to enhance accuracy. In addition, we have integrated an incremental learning module that can efficiently incorporate novel unknown traffic identified by telecommunication experts through the monitoring process. This unique approach provides an effective solution for identifying and alleviating DDoS. The module continuously improves the model's performance by incorporating new knowledge and adapting to new attack patterns. The proposed model can detect unknown DDoS attacks with a detection rate of over 99% on conventional attacks from CICIDS2017. The model's accuracy is further enhanced by 99.8% toward unknown attacks with the open datasets CICDDoS2019. [ABSTRACT FROM AUTHOR]

As the degree of elders' social activity and self-care ability depreciates, the potential risk for elderly people who live independently increases. The development of assistive services such as smart homes could likely provide them with a safer living environment. These systems collect sensor data to monitor residents' daily activities and provide assistance services accordingly. In order to do so, a smart home must understand its residents' daily activities and identify their periodic behavioral daily routine accordingly. However, existing solutions mainly focus on the temporal feature of daily activities and require prior labeling of where sensors are geographically deployed. In this study, we extract implicit spatial information from hidden correlations between sensors deployed in the environment and present a concept of virtual locations that establishes an abstract spatial representation of the physical living space so that prior labeling of the actual location of the sensors is not required. To demonstrate the viability of this concept, an unsupervised periodic behavioral routine discovery method that does not require any predefined location-specific sensor data for a smart home environment is proposed. The experimental results show that with the help of virtual location, the proposed method achieves high accuracy in activity discovery and significantly reduces the computation time required to complete the task relative to a system without virtual location. Furthermore, the result of simulated anomaly detection also shows that the periodic behavioral routine discovery system is more tolerant to differences in the way routines are performed. [ABSTRACT FROM AUTHOR]

The network system has become an indispensable component of modern infrastructure. DDoS attacks and their variants remain a potential and persistent cybersecurity threat. DDoS attacks block services to legitimate users by incorporating large amounts of malicious traffic in a short period or depleting system resources through methods specific to each client, causing the victim to lose reputation, finances, and potential customers. With the advancement and maturation of artificial intelligence technology, machine learning and deep learning are widely used to detect DDoS attacks with significant success. However, traditional supervised machine learning must depend on the categorized training sets, so the recognition rate plummets when the model encounters patterns outside the dataset. In addition, DDoS attack techniques continue to evolve, rendering training based on conventional data models unable to meet contemporary requirements. Since closed-set classifiers have excellent performance in cybersecurity and are quite mature, this study will investigate the identification of open-set recognition issues where the attack pattern does not accommodate the distribution learned by the model. This research proposes a framework that uses reconstruction error and distributes hidden layer characteristics to detect unknown DDoS attacks. This study will employ deep hierarchical reconstruction nets (DHRNet) architecture and reimplement it with a 1D integrated neural network employing loss function combined with spatial location constraint prototype loss (SLCPL) as a solution for open-set risks. At the output, a one-class SVM (one-class support vector machine) based on a random gradient descent approximation is used to recognize the unknown patterns in the subsequent stage. The model achieves an impressive detection rate of more than 99% in testing. Furthermore, the incremental learning module utilizing unknown traffic labeled by telecom technicians during tracking has enhanced the model's performance by 99.8% against unknown threats based on the CICIDS2017 Friday open dataset. [ABSTRACT FROM AUTHOR]

The job shop scheduling problem (JSSP) is a fundamental operational research topic with numerous applications in the real world. Since the JSSP is an NP-hard (nondeterministic polynomial time) problem, approximation approaches are frequently used to rectify it. This study proposes a novel biologically-inspired metaheuristic method named Coral Reef Optimization in conjunction with two local search techniques, Simulated Annealing (SA) and Variable Neighborhood Search (VNS), with significant performance and finding-solutions speed enhancement. The two-hybrid algorithms' performance is evaluated by solving JSSP of various sizes. The findings demonstrate that local search strategies significantly enhance the search efficiency of the two hybrid algorithms compared to the original algorithm. Furthermore, the comparison results with two other metaheuristic algorithms that also use the local search feature and five state-of-the-art algorithms found in the literature reveal the superior search capability of the two proposed hybrid algorithms. [ABSTRACT FROM AUTHOR]

DDoS (distributed denial of service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread and severe threat to the integrity of computer networks. DDoS can lead to system paralysis, making it difficult to troubleshoot. As a critical component of the creation of an integrated defensive system, it is essential to detect DDoS attacks as early as possible. With the popularization of artificial intelligence, more and more researchers have applied machine learning (ML) and deep learning (DL) to the detection of DDoS attacks and have achieved satisfactory accomplishments. The complexity and sophistication of DDoS attacks have continuously increased and evolved since the first DDoS attack was reported in 1996. Regarding the headways in this problem, a new type of DDoS attack, named adversarial DDoS attack, is investigated in this study. The generating adversarial DDoS traffic is carried out using a symmetric generative adversarial network (GAN) architecture called CycleGAN to demonstrate the severe impact of adversarial DDoS attacks. Experiment results reveal that the synthesized attack can easily penetrate ML-based detection systems, including RF (random forest), KNN (k-nearest neighbor), SVM (support vector machine), and naïve Bayes. These alarming results intimate the urgent need for countermeasures against adversarial DDoS attacks. We present a novel DDoS detection framework that incorporates GAN with a symmetrically built generator and discriminator defense system (SDGAN) to deal with these problems. Both symmetric discriminators are intended to simultaneously identify adversarial DDoS traffic. As demonstrated by the experimental results, the suggested SDGAN can be an effective solution against adversarial DDoS attacks. We train SDGAN on adversarial DDoS data generated by CycleGAN and compare it to four previous machine learning-based detection systems. SDGAN outperformed the other machine learning models, with a TPR (true positive rate) of 87.2%, proving its protection ability. Additionally, a more comprehensive test was undertaken to evaluate SDGAN's capacity to defend against unseen adversarial threats. SDGAN was evaluated using non-training data-generated adversarial traffic. SDGAN remained effective, with a TPR of around 70.9%, compared to RF's 9.4%. [ABSTRACT FROM AUTHOR]

DDoS (Distributed Denial of Service) attacks have become a pressing threat to the security and integrity of computer networks and information systems, which are indispensable infrastructures of modern times. The detection of DDoS attacks is a challenging issue before any mitigation measures can be taken. ML/DL (Machine Learning/Deep Learning) has been applied to the detection of DDoS attacks with satisfactory achievement. However, full-scale success is still beyond reach due to an inherent problem with ML/DL-based systems—the so-called Open Set Recognition (OSR) problem. This is a problem where an ML/DL-based system fails to deal with new instances not drawn from the distribution model of the training data. This problem is particularly profound in detecting DDoS attacks since DDoS attacks' technology keeps evolving and has changing traffic characteristics. This study investigates the impact of the OSR problem on the detection of DDoS attacks. In response to this problem, we propose a new DDoS detection framework featuring Bi-Directional Long Short-Term Memory (BI-LSTM), a Gaussian Mixture Model (GMM), and incremental learning. Unknown traffic captured by the GMM are subject to discrimination and labeling by traffic engineers, and then fed back to the framework as additional training samples. Using the data sets CIC-IDS2017 and CIC-DDoS2019 for training, testing, and evaluation, experiment results show that the proposed BI-LSTM-GMM can achieve recall, precision, and accuracy up to 94%. Experiments reveal that the proposed framework can be a promising solution to the detection of unknown DDoS attacks. [ABSTRACT FROM AUTHOR]

Autonomous underwater gliders are robotic underwater vehicles that do not require operator input. They are frequently deployed in large-scale, long-term ocean-sampling missions that take advantage of their buoyancy-driven engines and very low power consumption. A limitation of the buoyancy-driven engine design is that the glider must travel at slow speeds and is unable to confront strong upstream currents. Optimal path-planning to minimize the upstream-current effect and distance traversed is helpful to guide the glider navigating in the ocean. In a glider path-planning problem, reachability signifies that a minimal upstream-current effect along a path is achieved, while efficiency refers to optimizing path distance, to make it as short as possible. Reachability and efficiency are sometimes inconsistent goals in glider path-planning, and obtaining an optimal solution between both aspects of path-planning constitutes a multi-objective optimization problem. In order to discover an optimal path for glider safety, a parallel genetic approach to the glider path-planning is developed here. We present a novel scheme of upstream-current avoidance to solve the critical path-planning problem in deployment of multiple gliders. The benefits of the proposed approach are used to produce characteristic curves that express how improved cruising capability benefits path diversity. The reachability of an important scheme of upstream-current avoidance is validated by a maximum likelihood function. Moreover, a new crossover operator is proposed to encourage individuals of offspring in solution diversity. Numeric results demonstrate that the proposed path-planning approach determines an optimal path that reduces the upstream-current effect, and also shortens the cruising distance in multi-glider path-planning solutions. [ABSTRACT FROM AUTHOR]

In this work, an exponential effective function (EEF) is developed as fitness function applied in a hybrid-Genetic Algorithm (hybrid-GA) to propose a genetic-based effective approach to the glider path-planning of ocean-sampling mission in variable oceans. The proposed EEF is such an objective function that is able to be implemented in optimization algorithm such as Genetic Algorithm (GA) for evaluation of the fittest path. In consideration of the glider path-planning problem (GPP), two motivations are driven by the proposed approach to the glider path-planning in discovery of: (1) a reachable path with the upstream-current avoidance (UCA) in variable oceans; (2) an efficient path for the glider ocean-sampling mission. The exponential combination of the glider motion and current effects as well as the cruising distance benefits the path in terms of reachability and efficiency. The reachability is the first motivation to discover a reachable path implemented by the scheme of UCA, while the efficiency is the second motivation to shorten the cruising distance. Meanwhile, the stabilized path solution is accomplished by hybrid-GA. In variable oceans, currents severely impact the path solution and lead the global optimum to absence. Therefore, alternative is to discover an optimal path with the minimum upstream-current sub-paths to approximate the minimal cruising distance in the condition that the discovered cruising distance should be less than the glider cruising range. To deeply analyze the path reachability, two theorems are developed to verify the conditions of the downstream-current angle (DCA). To evaluate the path-planning performances, 6 state-of-the-art fitness functions are studied and used to make a fair comparison with the EEF in hybrid-GA. First of all, 112 scenarios are created in the restricted random current variations (RRCV). Secondly, 21 scenarios are created in the near-real Kuroshio Current of east Taiwan (KCET) introducing from an ocean prediction model. These scenarios are designed to evaluate fairly the EEF in hybrid-GA. Numeric results show that whether the RRCV or the KCET, the proposed EEF indeed is able to discover the optimal path with the benefits of reachability and efficiency. Therefore, the proposed genetic-based effective approach is well developed to solve the GPP in variable oceans. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. Methods and Materials: Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3+ xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R2, chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC. Results: Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R2 was satisfactory and corresponded well with the expected values. Conclusions: Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT. [ABSTRACT FROM AUTHOR]

DDoS (Distributed Denial of Service) has emerged as a serious and challenging threat to computer networks and information systems' security and integrity. Before any remedial measures can be implemented, DDoS assaults must first be detected. DDoS attacks can be identified and characterized with satisfactory achievement employing ML (Machine Learning) and DL (Deep Learning). However, new varieties of aggression arise as the technology for DDoS attacks keep evolving. This research explores the impact of a new incarnation of DDoS attack–adversarial DDoS attack. There are established works on ML-based DDoS detection and GAN (Generative Adversarial Network) based adversarial DDoS synthesis. We confirm these findings in our experiments. Experiments in this study involve the extension and application of the GAN, a machine learning framework with symmetric form having two contending neural networks. We synthesize adversarial DDoS attacks utilizing Wasserstein Generative Adversarial Networks featuring Gradient Penalty (GP-WGAN). Experiment results indicate that the synthesized traffic can traverse the detection systems such as k-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP) and Random Forest (RF) without being identified. This observation is a sobering and pessimistic wake-up call, implying that countermeasures to adversarial DDoS attacks are urgently needed. To this problem, we propose a novel DDoS detection framework featuring GAN with Dual Discriminators (GANDD). The additional discriminator is designed to identify adversary DDoS traffic. The proposed GANDD can be an effective solution to adversarial DDoS attacks, as evidenced by the experimental results. We use adversarial DDoS traffic synthesized by GP-WGAN to train GANDD and validate it alongside three other DL technologies: DNN (Deep Neural Network), LSTM (Long Short-Term Memory) and GAN. GANDD outperformed the other DL models, demonstrating its protection with a TPR of 84.3%. A more sophisticated test was also conducted to examine GANDD's ability to handle unseen adversarial attacks. GANDD was evaluated with adversarial traffic not generated from its training data. GANDD still proved effective with a TPR around 71.3% compared to 7.4% of LSTM. [ABSTRACT FROM AUTHOR]

A personalized search robot is developed as one major mechanism of a personalized software component retrieval system. This search robot automatically finds out the Web servers providing reusable software components, extracts needed software components from servers, classifies the extracted components, and finally establishes their indexing information for local component retrieval in the future. For adaptively tuning the performance of software component extraction and classification, an adaptive thesaurus and an adaptive classifier, realized by neuro-fuzzy models, are embedded in this search robot, and their learning algorithms are also developed. A prototype of the personalized software component retrieval system including the search robot has been implemented to confirm its validity and evaluate the performance. Furthermore, the framework of proposed personalized search robot could be extended to the search and classification of other kinds of Internet documents. [ABSTRACT FROM AUTHOR]

This editorial introduces the Special Issue, entitled "Deep Learning Applications with Practical Measured Results in Electronics Industries", of Electronics. Topics covered in this issue include four main parts: (I) environmental information analyses and predictions, (II) unmanned aerial vehicle (UAV) and object tracking applications, (III) measurement and denoising techniques, and (IV) recommendation systems and education systems. Four papers on environmental information analyses and predictions are as follows: (1) "A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence" by Panapakidis et al.; (2) "Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting" by Wan et al.; (3) "Modeling and Analysis of Adaptive Temperature Compensation for Humidity Sensors" by Xu et al.; (4) "An Image Compression Method for Video Surveillance System in Underground Mines Based on Residual Networks and Discrete Wavelet Transform" by Zhang et al. Three papers on UAV and object tracking applications are as follows: (1) "Trajectory Planning Algorithm of UAV Based on System Positioning Accuracy Constraints" by Zhou et al.; (2) "OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance" by Zhang et al.; (3) "Model Update Strategies about Object Tracking: A State of the Art Review" by Wang et al. Five papers on measurement and denoising techniques are as follows: (1) "Characterization and Correction of the Geometric Errors in Using Confocal Microscope for Extended Topography Measurement. Part I: Models, Algorithms Development and Validation" by Wang et al.; (2) "Characterization and Correction of the Geometric Errors Using a Confocal Microscope for Extended Topography Measurement, Part II: Experimental Study and Uncertainty Evaluation" by Wang et al.; (3) "Deep Transfer HSI Classification Method Based on Information Measure and Optimal Neighborhood Noise Reduction" by Lin et al.; (4) "Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets" by Chang et al.; (5) "High-Resolution Image Inpainting Based on Multi-Scale Neural Network" by Sun et al. Two papers on recommendation systems and education systems are as follows: (1) "Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing" by Sulikowski et al. and (2) "Generative Adversarial Network Based Neural Audio Caption Model for Oral Evaluation" by Zhang et al. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses reports on artificial intelligence, graph pattern mining, and algorithms.