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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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An LLM-Based Framework for Synthetic Data Generation 15

An LLM-Based Framework for Synthetic Data Generation

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15th IEEE Annual computing and communication Workshop and conference, ccWc 2025

作者： Goyal, Mandeep Mahmoud, Qusay H. Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C5 Canada

ISBN: (纸本)9798331507695

The demand for high-quality datasets is rapidly increasing across sectors such as healthcare, finance, and cybersecurity, yet challenges like data scarcity and privacy concerns persist. To address this, we introduce a framework for synthetic data generation that empowers users to create realistic datasets while maintaining privacy. The framework leverages fine-tuned Large Language Models (LLMs) and differential privacy techniques, including IBM's diffprivlib, to generate synthetic data that replicates real-world patterns without exposing sensitive information. A proof-of-concept platform has been constructed to facilitate seamless data generation and augmentation, making it particularly useful in scenarios where original datasets are inaccessible, scarce, or privacy-restricted. The platform supports the creation of datasets across five key categories, employing advanced methods to preserve data integrity while ensuring compliance with stringent privacy standards. By combining cutting-edge AI technologies with robust privacy-preserving techniques, this framework offers a practical solution for researchers and professionals seeking reliable synthetic data to drive innovation in data-sensitive fields. © 2025 IEEE.

关键词： Data integrity

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A Prediction Model for Short Price Jump in cryptocurrency Market 14

A Prediction Model for Short Price Jump in Cryptocurrency Ma...

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2024 IEEE 14th Annual computing and communication Workshop and conference, ccWc 2024

作者： Rajaei, Mohammadjavad Mahmoud, Qusay H. Ontario Tech University Department of Electrical Computer and Software Engineering OshawaL1G 0C5 Canada

ISBN: (纸本)9798350360134

In recent years, the global cryptocurrency market has attracted a diverse range of traders, from seasoned professionals to newcomers, resulting in a highly volatile environment. This volatility presents numerous opportunities for traders to capitalize on rapid price fluctuations. In this context, we introduce a Random Forest model designed to predict whether a coin will experience growth in the next trading candle, using several input features. We used Binance historical daily data from 1 Jan 2018 to 31 Dec 2021 to train our models and evaluated them using different time spans (varied between Jan 2022 to Oct 2023) as testing datasets. Moreover, we also used an over sampled training dataset to enhance the training process. Demonstrating notable precision, especially with a growth rate of 1%, the model has proven effective across various scenarios, consistently yielding profits. To be more specific, regarding the testing datasets of 1 to 31 Oct 2023, 1 Jul 2023 to 30 Sep 2023, and LSK/USDT from 1 Jan 2022 to 31 OcT 2023, using a growth rate of 1%, we achieved 18%, 30%, and 68% profits, respectively. This study underscores the potential for leveraging well-designed machine learning models to achieve significant profits, even in bearish market conditions. © 2024 IEEE.

关键词： Machine learning

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critical Role of Individual cell Temperature Monitoring in Mitigating Thermal Runaway and Reducing Accelerated Degradation in Lithium-Ion Batteries 14

Critical Role of Individual Cell Temperature Monitoring in M...

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14th Annual IEEE Applied Power Electronics conference and Exposition, APEc 2025

作者： Sharma, Mohit Samanta, Akash Locke, William Williamson, Sheldon Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C5 Canada

ISBN: (纸本)9798331516116

Thermal runaway in lithium-ion batteries (LIBs) is a critical risk, potentially leading to fires and explosions. This phenomenon can be triggered by overcharging, short-circuiting, physical damage, manufacturing defects, and overheating. Effective mitigation of thermal runaway relies on precise and close monitoring of individual cells within the battery pack, a task often complicated by the complexity and cost of implementing temperature sensors on each cell in automotive applications. This study demonstrates the importance of individual cell temperature monitoring by intentionally including an unhealthy cell (c#12) in a 14-cell LIB 21700 module equipped with an automotive-grade battery management system (BMS) from NXP®. Experimental results reveal that despite efficient BMS balancing, the weak cell exhibited a significantly higher temperature rise, with a final temperature difference of 6°c and a voltage difference of over 200 mV compared to healthy cells. These findings underscore the potential for thermal failure and runaway if individual cell temperatures are not closely monitored. Additionally, reliance on voltage-based control alone can lead to suboptimal battery pack utilization, as evidenced by a 5% capacity loss due to the weak cell. This research highlights the necessity of monitoring the temperature of each cell to prevent thermal runaway and ensure efficient battery performance. © 2025 IEEE.

关键词： Lithium-ion batteries

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Dispatching Shots Among Multiple Quantum computers: An Architectural Proposal 4

Dispatching Shots Among Multiple Quantum Computers: An Archi...

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4th IEEE International conference on Quantum computing and engineering, QcE 2023

作者： Bisicchia, Giuseppe Garcia-Alonso, Jose Murillo, Juan M. Brogi, Antonio University of Pisa Department of Computer Science Pisa Italy University of Extremadura Quercus Software Engineering Group Cáceres Spain

ISBN: (纸本)9798350343236

Quantum computing is continuously evolving and expanding. As time goes by, more and more Quantum computer implementations become available, each of them with their own features. In such a scenario, it can be difficult for developers to identify which Quantum computer is the most suitable for their needs. In this paper, different from current works presenting strategies to select only one Quantum computer, we propose a change of perspective. Indeed, due to the probabilistic nature of Quantum Mechanics, performing a computation in a Quantum computer usually requires iterating the same execution many times (called shots), to eventually end with a distribution of the final results. Leveraging this need, our architecture enables selecting many Quantum computers for the same circuit and spreading the shots among them. Such a mechanism offers also the possibility for developers to access the partial distributions obtained from the output of a subset of the selected computers. Finally, our architecture proposes to decouple the decision process from the actual execution of such a decision, by enabling developers to encode their specific custom policies. © 2023 IEEE.

关键词： software engineering

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Towards Efficient Auction Design with ROI constraints 23

Towards Efficient Auction Design with ROI Constraints

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23rd International conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Tang, Xinyu Lv, Hongtao Gao, Yingjie Wu, Fan Liu, Lei cui, Lizhen School of Software & C-FAIR Shandong University Jinan China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Software & C-FAIR Shandong University Shandong Research Institute of Industrial Technology Jinan China

Online advertising stands as a significant revenue source of the Internet. Recently, the trend among advertisers tilting towards the use of auto-bidding tools has heralded the emergence of a new model of bidders operating with constraints related to return on investment (ROI). However, most of the current research on ROI-constrained bidders in auction design only focuses on either the ROI constraints or values of bidders being private, while it is more practical to keep them both private in reality. Designing a truthful mechanism for bidders with both private values and ROI constraints introduces complexities because of the characteristics of designing mechanisms with multiple parameters. To remedy this, we divide bidders into binary classes: the traditional utility maximizers (UMs) who can be viewed as having an ROI constraint of 1, and the ROI-constrained bidders (RBs) who share a fixed ROI constraint denoted as γ. This framework retains the essence of multi-parameter mechanism design but transitions this into a more tractable form. Then we introduce a novel auction mechanism, cleverly combining the conventional VcG mechanism and an existing mechanism for public ROI-constrained bidders which is called cavallo's mechanism. Our mechanism can achieve an approximation ratio of 3/2 on social welfare. Additionally, we unearth new insights into the limitations posed by ROI constraints. When the ROI constraint γ exceeds 2, the lower bound of social welfare is 5/4;when it falls below 2, the 3+γ lower bound becomes (Equation presented). © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Autonomous agents

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Optimizing Resource Fragmentation in Virtual Network Function Placement Using Deep Reinforcement Learning

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 1475-1491页

作者： Mohamed, Ramy Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada École de technologie supérieure Department of Software and IT Engineering MontrealQCH3C 1K3 Canada

In the 6G wireless era, the strategical deployment of Virtual Network Functions (VNFs) within a network infrastructure that optimizes resource utilization while fulfilling performance criteria is critical for successfully implementing the Network Function Virtualization (NFV) paradigm across the Edge-to-cloud continuum. This is especially prominent when resource fragmentation -where available resources become isolated and underutilized- becomes an issue due to the frequent reallocations of VNFs. However, traditional optimization methods often struggle to deal with the dynamic and complex nature of the VNF placement problem when fragmentation is considered. This study proposes a novel online VNF placement approach for Edge/cloud infrastructures that utilizes Deep Reinforcement Learning (DRL) and Reward constrained Policy Optimization (RcPO) to address this problem. We combine DRL's adaptability with RcPO's constraint incorporation capabilities to ensure that the learned policies satisfy the performance and resource constraints while minimizing resource fragmentation. Specifically, the VNF placement problem is first formulated as an offline-constrained optimization problem, and then we devise an online solver using Neural combinatorial Optimization (NcO). Our method incorporates a metric called Resource Fragmentation Degree (RFD) to quantify fragmentation in the network. Using this metric and RcPO, our NcO agent is trained to make intelligent placement decisions that reduce fragmentation and optimize resource utilization. An error correction heuristic complements the robustness of the proposed framework. Through extensive testing in a simulated environment, the proposed approach is shown to outperform state-of-the-art VNF placement techniques when it comes to minimizing resource fragmentation under constraint satisfaction guarantees. © 2024 2024 The Authors.

关键词： constrained optimization

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comparative Analysis of Differential Privacy Implementations on Synthetic Data 15

Comparative Analysis of Differential Privacy Implementations...

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15th IEEE Annual computing and communication Workshop and conference, ccWc 2025

作者： Said, Huwida E. Mahmoud, Qusay H. Goyal, Mandeep Hashim, Faiza College of Technological Innovation Zayed University Department of Computing and Applied Technolgy Dubai United Arab Emirates Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C6 Canada College of Information Technology Uae University Department of Computer Science and Software Engineering AI-Ain United Arab Emirates

ISBN: (纸本)9798331507695

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments. © 2025 IEEE.

关键词： Data integrity

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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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Enhancing Viewer Engagement: Exploring Generative Long Short Term Memory in Dynamic Ad customization for Streaming Media 5

Enhancing Viewer Engagement: Exploring Generative Long Short...

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5th International conference on Smart Electronics and communication, IcOSEc 2024

作者： Ramagundam, Shashishekhar Karne, Niharika Troy University Software Development & Engineering in Content Discovery & Ai Department of computer Science Computer Science Montgomery Campus AL United States C & Rs Platform services DT Amtrak United States University of south alabama Department of electrical engineering Electrical engineering United States

ISBN: (纸本)9798331504403

In Ad customization, several approaches are employed to improve the relevance level of the message for monitoring the user performance according to the prior search history (e.g., retargeting) or by gathering particular information related to the customer (e.g., micro-targeting). Occasionally, the consumers view the ad developed by the other user, in this situation, the ads are viewed as something that can be related by the user. The quantity of information that is gathered openly or secretly can affect the user choice for the content displayed. However, the users remain as recipients of a strategy, that decides the best ad content. Therefore, an exploring Generative Artificial Intelligence-based Dynamic Ad customization model for enhancing viewer engagement from streaming media is developed. In this paper, the Generative Long Short-Term Memory (GenLSTM) model is developed for examining customized advertising then includes the customization of specific message elements by individuals, variation in the level of ad customization, and in some cases perceived collaboration with others. Here, the customization classification is defined based on the modification in the marketing mix element that is created by the customer proactively as opposed to the company. In regards to customized advertising, 'ad is something that happens with the viewer'. The customization increases based on the sense of task engagement, strength of the perceived customer relationship with the company, attitude toward the ad, and perceptions of company trust/integrity. Finally, the experimental analysis is conducted based on the relationship with the company, company care, company trust, and task engagement to attain the overall impact on ad customization. © 2024 IEEE.

关键词： Sales

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