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作者： Alqahtani, Ali Khan, Surbhi Bhatia Department of Networks and Communications Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Data Science School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Cyber-physical systems (CPS) and the Internet of Things (IoT) technologies link urban systems through networks and improve the delivery of quality services to residents. To enhance municipality services, information and communication technologies (ICTs) are integrated with urban systems. However, the large number of sensors in a smart city generates a significant amount of delicate data, like medical records, credit card numerics, and location coordinates, which are transported across a network to data centers for analysis and processing. This makes smart cities vulnerable to cyberattacks because of the resource constraints of their technology infrastructure. Applications for smart cities pose many security challenges, such as zero-day attacks resulting from exploiting weaknesses in various protocols. Therefore, this paper proposes an optimal hybrid transit search-cascade regional convolutional neural network (hybrid TS-Cascade R-CNN) to detect cyberattacks. The proposed model combines the hybrid transit-search approach with the cascade regional convolutional neural network to create an optimal solution for cyberattack detection. The cascade regional convolutional network uses a hybrid transit search algorithm to enhance the effectiveness of cyberattack detection. By integrating these two approaches, the system can leverage both global traffic patterns and local indicators to improve the accuracy of attack detection. During the training process, the proposed model recognizes and classifies malicious input even in the presence of sophisticated attacks. Finally, the experimental analysis is carried out for various attacks based on different metrics. The accuracy rate attained by the proposed approach is 99.2%, which is acceptable according to standards. © 2023 John Wiley & Sons Ltd.

关键词： Zero-day attack

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Towards the New XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence 27

Towards the New XAI: A Hypothesis-Driven Approach to Decisio...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Le, Thao Miller, Tim Sonenberg, Liz Singh, Ronal School of Computing and Information Systems The University of Melbourne Australia School of Electrical Engineering and Computer Science The University of Queensland Australia CSIRO's Data61 Australia

ISBN: (纸本)9781643685489

Prior research on AI-assisted human decision-making has explored several different explainable AI (XAI) approaches. A recent paper has proposed a paradigm shift calling for hypothesis-driven XAI through a conceptual framework called evaluative AI that gives people evidence that supports or refutes hypotheses without necessarily giving a decision-aid recommendation. In this paper, we describe and evaluate an approach for hypothesis-driven XAI based on the Weight of Evidence (WoE) framework, which generates both positive and negative evidence for a given hypothesis. Through human behavioural experiments, we show that our hypothesis-driven approach increases decision accuracy and reduces reliance compared to a recommendation-driven approach and an AI-explanation-only baseline, but with a small increase in under-reliance compared to the recommendation-driven approach. Further, we show that participants used our hypothesis-driven approach in a materially different way to the two baselines. © 2024 The Authors.

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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

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computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security information and Event Management systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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An IoMT-Based Incremental Learning Framework with a Novel Feature Selection Algorithm for Intelligent Diagnosis in Smart Healthcare

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 370-383页

作者： Sai, Siva Bhandari, Kartikey Singh Nawal, Aditya Chamola, Vinay Sikdar, Biplab Department of Computer Science and Information Systems Pilani Campus Pilani333031 India Microsoft Campus Gachibowli Telangana Hyderabad500032 India Department of Electrical and Electronics Engineering Pilani Campus Pilani333031 India Pilani Campus Pilani333031 India National University of Singapore Department of Electrical and Computer Engineering Cluny Road Singapore117583 Singapore

Several recent research papers in the Internet of Medical Things (IoMT) domain employ machine learning techniques to detect data patterns and trends, identify anomalies, predict and prevent adverse events, and develop personalized patient treatment plans. Despite the potential of machine learning techniques in IoMT to revolutionize healthcare, several challenges *** conventional machine learning models in the IoMT domain are static in that they were trained on some datasets and are being used for real-time inferencing data. This approach does not consider the patient's recent health-related data. In the conventional machine learning models paradigm, the models must be re-trained again, even to incorporate a few sets of additional samples. Also, since the training of the conventional machine learning models generally happens on cloud platforms, there are also risks to security and privacy. Addressing these several issues, we propose an edge-based incremental learning framework with a novel feature selection algorithm for intelligent diagnosis of patients. The approach aims to improve the accuracy and efficiency of medical diagnosis by continuously learning from new patient data and adapting to patient conditions over time, along with reducing privacy and security issues. Addressing the issue of excessive features, which might increase the computational burden on incremental models, we propose a novel feature selection algorithm based on bijective soft sets, Shannon entropy, and TOPSIS(Technique for Order Preference by Similarity to Ideal Solution). We propose two incremental algorithms inspired by Aggregated Mondrian Forests and Half-Space Trees for classification and anomaly detection. The proposed model for classification gives an accuracy of 87.63%, which is better by 13.61% than the best-performing batch learning-based model. Similarly, the proposed model for anomaly detection gives an accuracy of 97.22%, which is better by 1.76% than the best-performing b

关键词： Patient treatment

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Heart Disease Prediction Using Machine Learning Algorithms

Heart Disease Prediction Using Machine Learning Algorithms

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Priya, M. Anitha, A. Nallakaruppan, M.K. Raju, J. Raghavan, R. Srivastava, Divyansh School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350325188

Heart disease is one of the destructive infections that an enormous populace of individuals all over the planet grieves. The prediction of heart disease is very much urged because of increasing death rates. The conventional method of prediction isn't adequate for such a disease. Evolving a clinical determination framework based on machine learning concepts gives more exact conclusions than the conventional way for the prediction of such illness. In this paper, we have used different machine learning algorithms to accurately foresee whether an individual has a heart illness or not. The concepts of machine learning like linear prediction KNN, SVM, and, ANN to predict the probability of heart disease in an individual are used. The medical and general details of the person like BMI, alcohol and tobacco consumption, etc. are used to make the prediction. © 2023 IEEE.

关键词： ANN Heart Disease KNN Machine Learning Neural Network SVM

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Investigating the Determinants of Compliance Intention in Behavior Change Support systems 29

Investigating the Determinants of Compliance Intention in Be...

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29th Annual Americas Conference on information systems: Diving into Uncharted Waters, AMCIS 2023

作者： Ekpezu, Akon Obu Oinas-Kukkonen, Harri Wiafe, Isaac Faculty of Information Technology and Electrical Engineering Oulu Advanced Research on Service and Information Systems University of Oulu Finland Department of Computer Science University of Ghana Ghana

ISBN: (纸本)9781713893592

The effectiveness of behavior change support systems (BCSS) in promoting health and well-being is unflinching. However, its long-term effectiveness is hindered by non-compliance. Research in BCSS that focuses on compliance is scarce, particularly the determinants of compliance intention are not well established. To address this gap, this study investigated the determinants of compliance intention in BCSS by extending the persuasive system design model to include competence, effectiveness, enjoyment, and persuasiveness. By surveying a sample of prospective BCSS users (N=234), partial least squares structural equation modeling was used to evaluate the proposed model. The findings suggest that the prospective users’ perceptions of primary task support and dialogue support, as well as social support and credibility support significantly influenced their perceptions of self-competence and system effectiveness respectively. These perceptions influenced enjoyment, then persuasiveness, and finally compliance intention. Future studies will validate these findings on the compliance behavior of BCSS. © 2023 29th Annual Americas Conference on information systems, AMCIS 2023. All rights reserved.

关键词： Behavior Change Support systems Compliance Intention Persuasive systems Design Persuasive Technology Physical Activity

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Medical Reports Simplification Using Large Language Models 23th

Medical Reports Simplification Using Large Language Models

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23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Almezhghwi, Khaled Hassan, Morad Ali Ghadedo, Adel Belhaj, Fairouz Shwehdi, Rabei Electrical and Electronics Engineering College of Electronics Technology Tripoli Tripoli Libya Computer Science Bani Waleed University Bani Waleed Libya Computer Science College of Electronics Technology Tripoli Tripoli Libya Computer Information Systems Higher Institute of Science and Technology Tripoli Libya Electrical and Electronics Engineering College of Electronics Technology Tripoli Tripoli Libya

ISBN: (纸本)9783031648120

Making medical reports easily understandable for a wider audience is a significant endeavor, and the recent advancements in deep learning and large language models offer a promising solution. In our research, we have introduced a fine-tuned Text-to-Text Transformer (T5) model to efficiently summarize these medical reports. Our model is trained and assessed on the publicly accessible Indiana Dataset. To gauge its effectiveness, we employ the ROUGE metrics. The outcomes we've achieved hold promise for the potential of this approach in enhancing medical report summarization. By harnessing the power of cutting-edge technology, we aim to make medical information more accessible and comprehensible to everyone. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Tolerant Tracking Control Protocol for PMSM Based on Policy Iteration Algorithm and Fault Compensation 16th

Tolerant Tracking Control Protocol for PMSM Based on Polic...

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16th International Conference on Social Robotics, ICSR + InnoBiz 2024

作者： Yan, Shuya Li, Xiaocong Qian, Huaming Ma, Jun Al Mamun, Abdullah College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Guangzhou China Information Science and Technology Eastern Institute of Technology Ningbo China

ISBN: (纸本)9789819611508

In robotics applications, ensuring reliable performance in the presence of actuator faults is essential for maintaining system safety and reliability. This paper presents a tolerant tracking control method for permanent magnet synchronous motors (PMSMs) based on adaptive dynamic programming and fault compensation. The method simultaneously considers tracking accuracy and energy consumption through a policy iteration algorithm. In the optimality analysis of the algorithm, more relaxed conditions are provided to demonstrate that the performance function can converge to a near-optimal value within a finite number of iterations. In practical implementation, an actor-critic network is used to approximate the performance function and control protocol, alongside a fault detection mechanism based on an expanded time horizon, which achieves fault detection from arbitrary initial values. The effectiveness of the proposed algorithm is verified using a high-fidelity PMSM model in Simulink. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Permanent magnets

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DaC-GANSAEBF:Divide and Conquer-Generative Adversarial Network-Squeeze and Excitation-Based Framework for Spam Email Identification

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computer Modeling in engineering & sciences 2025年第3期142卷 3181-3212页

作者： Tawfeeq Shawly Ahmed A.Alsheikhy Yahia Said Shaaban M.Shaaban Husam Lahza Aws I.Abu Eid Abdulrahman Alzahrani Department of Electrical Engineering Faculty of Engineering at RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Information Technology College of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information and Communication Technology College of Computing StudiesArab Open UniversityKuwait BranchKuwait City13033Al-SafatState of Kuwait Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia

Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable security *** spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers,resulting in user dissatisfaction and potential data *** address this issue,we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework(DaC-GANSAEBF),an innovative deep-learning model designed to identify spam *** framework incorporates cutting-edge technologies,such as Generative Adversarial Networks(GAN),Squeeze and Excitation(SAE)modules,and a newly formulated Light Dual Attention(LDA)mechanism,which effectively utilizes both global and local attention to discern intricate patterns within textual *** approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller,independently evaluated *** model underwent training and validation using four publicly available benchmark datasets,achieving an impressive average accuracy of 98.87%,outperforming leading methods in the *** findings underscore the resilience and scalability of DaC-GANSAEBF,positioning it as a viable solution for contemporary spam detection *** framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.

关键词： Email spam fraud light dual attention squeeze and excitation divide and conquer-generative adversarial network-squeeze and excitation-based framework security

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Protecting Your Attention during Distributed Graph Learning: Efficient Privacy-Preserving Federated Graph Attention Network

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IEEE Transactions on information Forensics and Security 2025年 20卷 1949-1964页

作者： Zhou, Jinhao Wu, Jun Ni, Jianbing Wang, Yuntao Pan, Yanghe Su, Zhou Waseda University Graduate School of Information Production and Systems Fukuoka808-0135 Japan Queen's University Department of Electrical and Computer Engineering KingstonONK7L 3N6 Canada Xi'An Jiaotong University School of Cyber Science and Engineering Xi'an710049 China

Federated graph attention networks (FGATs) are gaining prominence for enabling collaborative and privacy-preserving graph model training. The attention mechanisms in FGATs enhance the focus on crucial graph features for improved graph representation learning while maintaining data decentralization. However, these mechanisms inherently process sensitive information, which is vulnerable to privacy threats like graph reconstruction and attribute inference. Additionally, their role in assigning varying and changing importance to nodes challenges traditional privacy methods to balance privacy and utility across varied node sensitivities effectively. Our study fills this gap by proposing an efficient privacy-preserving FGAT (PFGAT). We present an attention-based dynamic differential privacy (DP) approach via an improved multiplication triplet (IMT). Specifically, we first propose an IMT mechanism that leverages a reusable triplet generation method to efficiently and securely compute the attention mechanism. Second, we employ an attention-based privacy budget that dynamically adjusts privacy levels according to node data significance, optimizing the privacy-utility trade-off. Third, the proposed hybrid neighbor aggregation algorithm tailors DP mechanisms according to the unique characteristics of neighbor nodes, thereby mitigating the adverse impact of DP on graph attention network (GAT) utility. Extensive experiments on benchmarking datasets confirm that PFGAT maintains high efficiency and ensures robust privacy protection against potential threats. © IEEE.

关键词： Differential privacy

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