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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 220-231页

作者： Abu-Nassar, Ahmad M. Morsi, Walid G. Electrical Computer and Software Engineering Department Faculty of Engineering and Applied Science OshawaONL1G 0C5 Canada

Transportation electrification plays an important role in the operation of the smart grid through the integration of the electric vehicle fast charging stations (EVFCSs), which allows the electric vehicles to provide regulation services to the grid through the vehicle-to-grid (V2G) concept. However, such an integration makes smart grid assets prone to cyber vulnerability threats. In this paper, a cyber-physical attack detection approach is developed to early detect such attacks. The proposed approach combines the continuous wavelet transform (CWT) and the convolution neural network (CNN) to provide an effective detection technique. The proposed detection approach has undergone rigorous testing that considered 420 realistic operational scenarios. Unlike in previous work, the proposed detection approach was found to be effective in automatically learning the salient features from the data as well as identifying the frequency bands that hold such features and using them in the classification process. Furthermore, this work investigated the cyber-attack detection accuracy using different time resolutions of smart meters. The results have shown that the proposed approach effectively detects cyber-physical attacks with an accuracy of 99.76% and a low computational time of 1.8 seconds. © 2024 IEEE.

关键词： Feature extraction

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Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

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computers, Materials & Continua 2024年第2期78卷 2287-2308页

作者： Shiyan Zhang Ruohan Xu Zhangbo Xu Cenhua Yu Yuyang Jiang Yuting Zhao School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing100876China College of Computer Science and Technology Tianjin UniversityTianjin300072China Institute of Artificial Intelligence Nankai UniversityTianjin300071China College of Information Science and Engineering East China University of Science and TechnologyShanghai200237China

The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and *** this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain *** to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward *** the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the *** addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+*** a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network *** LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.

关键词： Multi-Domain data center networks autonomous routing

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Understanding Blockchain Trilemma, Causes and Solutions 17

Understanding Blockchain Trilemma, Causes and Solutions

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Chaudhary, Monu Bhunia, Suman Miami University Department of Computer Science and Software Engineering OxfordOH United States

ISBN: (纸本)9798350351637

With extensive research and development in blockchain technology, the concern about scalability, security, and decentralization is still evident. Blockchain trilemma describes that it is realistically impossible to simultaneously achieve a high level of decentralization, security, and scalability. However, new developments in the sector of blockchain are making progress in simultaneous achievement of these three aspects. Layer 1 blockchain is the base level of blockchain network architecture. It includes blockchain networks like Bitcoin, Ethereum, etc. However, the Layer 1 blockchain network faces scalability issues. Layer 2 blockchain is a network built on top of Layer 1 blockchain to help eliminate scalability issues. This paper presents a comparative analysis of Layer 1 and Layer 2 blockchain networks and different approaches to solving the blockchain trilemma in Layer 1 and Layer 2 blockchain networks. From the study, we found that Layer 2 blockchain networks are easier and faster to implement. © 2024 IEEE.

关键词： Blockchain Blockchain Trilemma Decentralization Scalability Security

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Ethical Aspects of ChatGPT in software engineering Research

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 254-267页

作者： Akbar, Muhammad Azeem Khan, Arif Ali Liang, Peng Lappeenranta-Lahti University of Technology Software Engineering Department Lappeenranta15210 Finland University of Oulu M3S Empirical Software Engineering Research Unit Oulu90014 Finland Wuhan University School of Computer Science Wuhan430072 China

ChatGPT can improve software engineering (SE) research practices by offering efficient, accessible information analysis, and synthesis based on natural language interactions. However, ChatGPT could bring ethical challenges, encompassing plagiarism, privacy, data security, and the risk of generating biased or potentially detrimental data. This research aims to fill the given gap by elaborating on the key elements: motivators, demotivators, and ethical principles of using ChatGPT in SE research. To achieve this objective, we conducted a literature survey, identified the mentioned elements, and presented their relationships by developing a taxonomy. Furthermore, the identified literature-based elements (motivators, demotivators, and ethical principles) were empirically evaluated by conducting a comprehensive questionnaire-based survey involving SE researchers. In addition, we employed an interpretive structure modeling approach to analyze the relationships between the ethical principles of using ChatGPT in SE research and develop a level-based decision model. We further conducted a cross-impact matrix multiplication applied to classification analysis to create a cluster-based decision model. These models aim to help SE researchers devise effective strategies for ethically integrating ChatGPT into SE research by following the identified principles by adopting the motivators and addressing the demotivators. The findings of this study will establish a benchmark for incorporating ChatGPT services in SE research with an emphasis on ethical considerations. © 2020 IEEE.

关键词： software engineering

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Detection of Conspiracy vs. Critical Narratives and Their Elements using NLP 25

Detection of Conspiracy vs. Critical Narratives and Their El...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Albladi, Aish Seals, Cheryl D. Department of Computer Science and Software Engineering Auburn University AuburnAL United States

The growing use of digital media or social media has given a platform to the people to deliver their ideas and viewpoints openly. It is facilitating the rapid spread of contrasting opinions openly. Ultimately, this has developed echo chambers or polarized platforms. These platforms either encourage conspiracy theories or critical narratives. So, nowadays, it has become crucial for people to be aware of being part of such narratives. Moreover, it is also necessary for the organisations to identify the conspiracy creators or the ones who do not possess a conspiracy mentality. This can be made possible by the use of natural language processing techniques to do this task automatically. This study presents the solutions to the two most pressing challenges in today's era. The subtask one of this study provides a solution for the identification of conspiracy narratives and critical narratives using the BERT model. Our model achieved an MCC score of 0.80. Secondly, the other subtask helps to recognize the span of the topic relevant to the important constituents of both categories of oppositional narratives, i.e., agent, facilitator, victim, campaigner, objective, negative effect. The overall accuracy of the model is 95%. These models can be used for the development of an algorithm that can automatically classify the conspiracy theories and critical narratives. © 2024 Copyright for this paper by its authors.

关键词： Social networking (online)

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A Multimodal Method for Semi-Biometric Information Based User Identification in AR and VR Applications

A Multimodal Method for Semi-Biometric Information Based Use...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Li, Han Lyu, Ke Dossett, Owen Feng, Xianglong Miami University Department of Computer Science and Software Engineering Oxford45056 United Kingdom

ISBN: (纸本)9798350367942

Virtual Reality (VR) and Augmented Reality (AR) have witnessed a surge in popularity, revolutionizing various industries and enhancing user experiences. As these technologies continue to evolve, ensuring secure user identification becomes increasingly important. However, existing identification methods often come with vulnerabilities or require costly hardware implementations. To address these challenges, we propose a novel semi-biometric information-based user identification approach leveraging multimodal techniques. By analyzing the user's viewing patterns and gaze behavior in the runtime, we can accurately identify individuals. This approach offers a promising solution for seamless user identification in VR and AR applications, without compromising on user experience or requiring expensive hardware modifications. To verify the efficiency of our proposed algorithm, we test our algorithm using a public dataset, the result of which shows a high classification accuracy. Additionally, beyond traditional evaluation metrics, we perform a transferability test, demonstrating the adaptability of our solution to new scenarios with robust generality. © 2024 IEEE.

关键词： Biometrics

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Neural Operator-Based Framework for Time Efficient Denoising of Displacement Fields in Ultrasound Elastography

Neural Operator-Based Framework for Time Efficient Denoising...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhu, Yihong Peng, Bo School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China

ISBN: (纸本)9781665410205

In ultrasound elastography, the noise present in the measured displacement fields has been a critical factor affecting the quality of the strain or elastic distribution reconstruction. Existing partial differential equation (PDE) based denoising algorithms can effectively remove noise to some extent but are limited by slow solving speeds. To address this challenge, we introduce a neural operator-based framework for denoising measured displacement fields in ultrasound elastography. By utilizing neural operators to learn a general solution operator for the denoising equation, the framework aims to achieve fast and accurate denoising of displacement fields. Experiments conducted on simulated data and tissue-mimicking phantom data demonstrate that this method performs comparably to traditional methods across various metrics. In particular, the proposed approach achieves processing speeds approximately 30 to 577 times faster than the finite difference method (FDM) in various data dimensions, making it a highly effective solution for rapid and scalable data processing in ultrasound elastography. © 2024 IEEE.

关键词： Ultrasonic applications

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Fed-BRMC: Byzantine-Robust Federated Learning via Dual-Model Contrastive Detection

Fed-BRMC: Byzantine-Robust Federated Learning via Dual-Model...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Gan, Xiaoyun Lu, Peng Gan, Shanyu Xian, Youqian Peng, Kaichen Liu, Peng Li, Dongcheng School of Computer Science and Engineering School of Software Guangxi Normal University Guilin China

ISBN: (纸本)9781665410205

Federated Learning (FL) is a distributed privacy-protecting machine learning paradigm that enables collaborative training among multiple parties without the need to share raw data. This mode of training renders FL particularly susceptible to attacks. A pivotal challenge within FL is the defense against Byzantine attacks, which can alter the model aggregation process and ultimately lead to the failure of Federated Learning convergence. Existing Byzantine defense mechanisms often result in diminished model accuracy in scenarios characterized by non-independent and identically distributed (non-IID) data. To alleviate this issue, we propose a novel defense approach named Fed-BRMC. This method combines a self-developed federated Dual-Model Contrastive Detection technique with Similarity Detection methods, leveraging historical update information from users to deeply mine the characteristics of model parameters, effectively distinguishing between Byzantine and honest users. Extensive experimentation has shown that Fed-BRMC significantly improves user identification accuracy in non-IID data scenarios compared to existing methods and notably enhances the global model accuracy of Byzantine-robust Federated Learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Applied Technology Research on the Construction of Bidirectional Knowledge Graph Curriculum System Based on SmartKG 6

Applied Technology Research on the Construction of Bidirecti...

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6th International Workshop on Artificial Intelligence and Education, WAIE 2024

作者： Jiang, Su Luo, Wei Cui, Hengji School of Computer Science and Software Engineering Dalian Neusoft University of Information Dalian China

ISBN: (纸本)9798350378894

Amidst the rapid advancements in artificial intelligence technology, it is imperative to apply these technological developments to the realm of education to enhance information-based teaching methodologies. This article introduces a knowledge management framework that aims to visualize curriculum knowledge systems using knowledge graphs in the educational sector. Taking courses like Fundamentals of computer Systems as exemplars, the article demonstrates the techniques for constructing knowledge graphs. Additionally, SmartKG's ease of deployment and application allows students to construct their own visual knowledge graphs, fostering a deeper comprehension of knowledge and fostering the profound integration of AI and teaching. Through the analysis of the curriculum system and students' self-constructed knowledge graphs, the efficacy of utilizing knowledge graphs in learning has been validated, propelling the application and practice of intelligent classroom teaching. © 2024 IEEE.

关键词： Knowledge graph

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Unsupervised Insider Threat Detection Using Multi-Head Self-Attention Mechanisms 4

Unsupervised Insider Threat Detection Using Multi-Head Self-...

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4th Intelligent Cybersecurity Conference, ICSC 2024

作者： Bertrand, Simon Germain, Pascal Tawbi, Nadia Laval University Dept. of Computer Science & Software Engineering Québec Canada

ISBN: (纸本)9798350354775

We propose an unsupervised insider threat detection system that learns normal user behaviors through audit data using neural networks equipped with multi-head self-attention mechanisms. The attention mechanisms learn precise normal user behaviors using large event windows. The key idea is to consider a sequence abnormal when it exhibits some events that are not likely to happen given the preceding events sequence. In addition, our method provides insights into detected threats through an event-based scoring system to facilitate threat understanding by security experts. Furthermore, the proposed solution does not require intensive audit logs pre-processing, such as manual domain knowledge-rich features extraction and data balancing. Thus, the proposed approach is easy to implement and use across organizations regardless of their expertise level. The proposed solution also provides the ability to detect threats in real-time. On the benchmark dataset CERT version 4.2, our solution, combined with hyperparameter search by Bayesian optimization, outperforms the previous state-of-the-art approaches with an area under the ROC curve of 97.23%, a recall of 91.51%, an accuracy of 93.13% and a false positive rate of 6.8%. © 2024 IEEE.

关键词： Deep neural networks

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