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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： LI ZHENJIANG LI HONGWEI QIAN XINYUAN School of Computer Science and Engineering (School of Cyber Security) University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Privacy-preserving data aggregation (P2DA) is a technique that enables the simultaneous availability of data and the preservation of privacy in smart grid systems. However, existing methods are only capable of fulfilling a subset of the essential criteria for the development of smart grids. The current methodologies are based on a number of strong assumptions, or else they result in an increase in the computational and communication overhead. To address this challenge, this paper introduces FEDA, a P2DA scheme based on functional encryption for smart grids, offering efficient encryption and decryption processes, along with verifiable aggregation. The results of our experiments demonstrate that our approach markedly reduces the computational and communication overheads in comparison to existing methods. Furthermore, it enhances the encryption speed by approximately eightfold and improves the aggregation and decryption efficiencies while maintaining a high level of security.

关键词： Privacy Fault tolerance Data privacy Fault tolerant systems Information processing Data aggregation Media Encryption Smart grids security

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Personalized Food Recommendations: A Machine Learning Model for Enhanced Dining Choices 14

Personalized Food Recommendations: A Machine Learning Model ...

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14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Al-Hubaishi, Mohammed Ali, Musab A. M. Tahir, Nooritawati Md Halic University Department of Computer Engineering Istanbul Turkey University of Technology and Applied Sciences College of Computing and Information Sciences Oman International Information Technology University Department of Computer Engineering and Information Security Kazakhstan Universiti Teknologi MARA Malaysia Universiti Teknologi MARA College of Engineering Malaysia Riyadh Saudi Arabia

ISBN: (纸本)9798350364507

This research introduces an innovative approach to enhance dining experiences through a personalized meal suggestion system. Our method leverages decision tree-based machine learning to predict user preferences by analyzing various attributes. The aim is to assist individuals in making optimal food choices at restaurants, thereby enriching their culinary journey. QR codes are integrated into an Android restaurant application, facilitating convenient access to the food menu. User inquiries are efficiently managed through a MySQL database accessible across multiple platforms, including a Java GUI, an Android app, and a dedicated website catering to both user and administrator roles. © 2024 IEEE.

关键词： Adversarial machine learning

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New Perspectives on Data Exfiltration Detection for Advanced Persistent Threats Based on Ensemble Deep Learning Tree 19

New Perspectives on Data Exfiltration Detection for Advanced...

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19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Cai, Xiaojuan Koide, Hiroshi Department of Information Science and Technology Information Science and Electrical Engineering Kyushu University Fukuoka Japan Section of Cyber Security for Information Systems Research Institute for Information Technology Kyushu University Fukuoka Japan

ISBN: (纸本)9789897586729

Data exfiltration of Advanced Persistent Threats (APTs) is a critical concern for high-value entities such as governments, large enterprises, and critical infrastructures, as attackers deploy increasingly sophisticated and stealthy tactics. Although extensive research has focused on methods to detect and halt APTs at the onset of an attack (e.g., examining data exfiltration over Domain Name System tunnels), there has been a lack of attention towards detecting sensitive data exfiltration once an APT has gained a foothold in the victim system. To address this gap, this paper analyzes data exfiltration detection from two new perspectives: exfiltration over a command-and-control channel and limitations on exfiltration transfer size, assuming that APT attackers have established a presence in the victim system. We introduce two detection mechanisms (Transfer Lifetime Volatility & Transfer Speed Volatility) and propose an ensemble deep learning tree model, EDeepXGB, based on eXtreme Gradient Boosting, to analyze data exfiltration from these perspectives. By comparing our approach with eight deep learning models (including four deep neural networks and four convolutional neural networks) and four traditional machine learning models (Naive Bayes, Quadratic Discriminant Analysis, Random Forest, and AdaBoost), our approach demonstrates competitive performance on the latest public real-world dataset (Unraveled-2023), with Precision of 91.89%, Recall of 93.19%, and F1-Score of 92.49%. Copyright © 2023 by SCITEPRESS - Science and technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Sensitive data

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cybersecurity of SMART Technologies Using Deep Learning Methods 8

Cybersecurity of SMART Technologies Using Deep Learning Meth...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Milov, Oleksandr Rahimova, Irada Havrylova, Alla Golovashych, Sergii Aksonova, Iryna Pecherytsia, Dmytro National Technical University 'Kharkiv Polytechnic Institute' Department of Cyber Security Kharkiv Ukraine Azerbaijan Technical University Department of 'Computer Technologies' Baku Azerbaijan National Technical University 'Kharkiv Polytechnic Institute' Department of Software Engineering and Intelligent Control Technologies Kharkiv Ukraine

ISBN: (纸本)9798350354423

The concept of 'smart', the concept of smart technology, and its main elements are considered. An analysis of the factors influencing the formation and development of the concept of smart technology is provided. Aspects of the development of smart technology are highlighted. A description of deep learning methods is given and their classification is proposed. The areas of application of the described methods in various cybersecurity applications are identified, including detection of intrusions and malware, analysis of network traffic and some other tasks related to smart technologies. © 2024 IEEE.

关键词： Network intrusion

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Evaluation of the Value and the Contribution of Virtual Reality Technologies to Skill Training

Evaluation of the Value and the Contribution of Virtual Real...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Abdullah, Muhammad Almansour, Ammar Yaser Shawal, Nukhba Hussain, Muhammad Zunnurain Almansour, Bashar Yaser Hasan, Muhammad Zulkifl University of Central Punjab Department of Computer Sciences Lahore Pakistan Oman College of Management and Technology Department of Administrative and Financial Sciences Barka Oman Bahria University Lahore Campus Department of Computer Sciences Pakistan Muscat University Faculty of Business and Management Muscat Oman Cyber Security Center Universiti Putra Malaysia

ISBN: (纸本)9798350369106

In a rapidly evolving professional landscape, Virtual Reality (VR) emerges as a transformative device in talent training and improvement. This paper affords a complete analysis of VR's effect on schooling packages across multiple domains. We compare VR to traditional schooling techniques in terms of effectiveness, cost, and employee engagement, highlighting the strengths and limitations of both approaches. Our findings suggest VR shows higher effectiveness in problem-solving and teamwork, but traditional methods retain an edge in creativity. We conclude that VR technology holds a pivotal role in the future of professional development. © 2024 IEEE.

关键词： Virtual environments

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Performance Analysis of Intelligent Reflecting Surface Assisted Wireless Communication System

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computer Modeling in engineering & Sciences 2023年第10期137卷 775-787页

作者： Weiqiang Tan Quanquan Zhou Weijie Tan Longcheng Yang Chunguo Li School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China Key Laboratory of Advanced Manufacturing Technology Ministry of EducationGuizhou UniversityGuiyang550025China Sichuan Key Laboratory of Indoor Space Layout Optimization and Security Guarantee Chengdu Normal UniversityChengdu611130China School of Information Science and Engineering Southeast UniversityNanjing210096China

In this paper,we investigate the end-to-end performance of intelligent reflecting surface(IRS)-assisted wireless communication *** consider a system in which an IRS is deployed on a uniform planar array(UPA)configuration,including a large number of reflecting elements,where the transmitters and receivers are only equipped with a single *** objective is to analytically obtain the achievable ergodic rate,outage probability,and bit error rate(BER)of the ***,to maximize the system’s signal-to-noise ratio(SNR),we design the phase shift of each reflecting element and derive the optimal reflection phase of the IRS based on the channel state information(CSI).We also derive the exact expression of the SNR probability density function(p.d.f.)and show that it follows a non-central Chi-square *** the p.d.f.,we then derive the theoretical results of the achievable rate,outage probability,and *** accuracy of the obtained theoretical results is also verified through numerical *** shown that the achievable rate,outage probability,and BER could be improved by increasing the number of reflecting elements and choosing an appropriate SNR ***,we also find that the IRS-assisted communication system achieves better performance than the existing end-to-end wireless communication.

关键词： Massive MIMO intelligent reflecting surface uniform planar array achievable ergodic rate

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Accurate Distinction of Weak and Strong AC Grids for Emerging Hierarchical-Infeed LCC-UHVDC Systems

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 772-777页

作者： Hao Xiao Xianzhong Duan Yi Zhang Department of Electrical and Computer Engineering University of ManitobaWinnipeg R3T 2N2ManitobaCanada State Key Laboratory of Advanced Electromagnetic Engineering and Technology Hubei Electric Power Security and High Efficiency Key LaboratorySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074HubeiChina RTDS Technologies Inc. Winnipeg R3T 2E1ManitobaCanada

Distinction of weak and strong AC grids for emerging hierarchical-infeed LCC-UHVDC systems is important for planning and operation departments. However, accuracy of earlier distinction methods is limited as they were developed by empirical reasoning without rigorous theoretical analysis. Hence in this letter, hierarchical-infeed interactive effective short-circuit ratio (HIESCR) index is first used for strength evaluation of HIDC systems with complex inter-inverter interactions considered. Boundary HIESCR (BHIESCR) is also introduced in the proposed distinction method of weak and strong AC grids. That is, weak (or strong) AC grids are, respectively, identified when HIESCR is less (or greater) than BHIESCR. Second, it is shown BHIESCR remains almost unchanged as 3.0 versus various system parameters and rated operation variables based on rigorous theoretical analysis. This salient feature makes the proposed method more accurate than earlier methods. Finally, the proposed method is validated by simulations based on the PSCAD/EMTDC program.

关键词： Hierarchical-Infeedinteractive effective short-circuit ratio hierarchical-infeed LCC-UHVDC systems inter-inverter interactions strong AC grids weak AC grids

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Machine learning-optimized titanium-based broadband absorber with high-efficiency performance across visible and infrared wavelengths

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Optical and Quantum Electronics 2025年第2期57卷 1-24页

作者： Wekalao, Jacob Muheki, Jonas Mandela, Ngaira Kabarokole, Pelluce Makoye, Paschal Department of Optics and Optical Engineering University of Science and Technology of China Hefei230026 China Department of Biological and Medical Physics 4302 University Dr HoustonTX77004 United States School of Digital Forensics and Cyber Security National Forensic Sciences University Gujarat Gandhinagar382007 India Department of Materials Science and Engineering University of Houston HoustonTX77204-4004 United States

This study introduces an advanced broadband absorber design featuring titanium-based square ring resonators on silicon dioxide substrates, optimized for superior absorption performance across visible and infrared wavelengths. The proposed absorber leverages a metal–insulator–metal configuration with a titanium resonator layer, SiO2 substrate, and tungsten ground layer, achieving over 94% absorption across the 0.7–4 μm wavelength range, with peak efficiency surpassing 99% at 2.142 μm. Unlike conventional designs relying on noble metals, the proposed absorber utilizes titanium, offering a cost-effective, thermally stable, and scalable solution suitable for high-temperature applications. The key novelty of this work lies in integrating machine learning, specifically K-Nearest Neighbour (KNN) regression, to predict and optimize the absorption characteristics, achieving R2 values of up to 0.99. This approach facilitates rapid design iterations, ensuring robust performance under varying structural and environmental conditions. Furthermore, the absorber demonstrates exceptional angular and polarization independence, maintaining high efficiency under both transverse electric (TE) and transverse magnetic (TM) polarizations. These attributes make the proposed design an innovative and versatile solution for applications in solar energy harvesting, thermal management, and broadband photonic sensing. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： High temperature applications

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Divide, Conquer, and Coalesce: Meta Parallel Graph Neural Network for IoT Intrusion Detection at Scale 24

Divide, Conquer, and Coalesce: Meta Parallel Graph Neural Ne...

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33rd ACM Web Conference, WWW 2024

作者： Ding, Hua Chen, Lixing Li, Shenghong Bai, Yang Zhou, Pan Qu, Zhe School of Cyber Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Cyber Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China School of Computer Science and Engineering Central South University Hunan Changsha China

ISBN: (纸本)9798400701719

This paper proposes Meta Parallel Graph Neural Network (MPGNN) to establish a scalable Network Intrusion Detection System (NIDS) for large-scale Internet of Things (IoT) networks. MPGNN leverages a meta-learning framework to optimize the parallelism of GNN-based NIDS. The core of MPGNN is a coalition formation policy that generates meta-knowledge for partitioning a massive graph into multiple coalitions/subgraphs in a way that maximizes the performance and efficiency of parallel coalitional NIDSs. We propose an offline reinforcement learning algorithm, called Graph-Embedded Adversarially Trained Actor-Critic (G-ATAC), to learn a coalition formation policy that jointly optimizes intrusion detection accuracy, communication overheads, and computational complexities of coalitional NIDSs. In particular, G-ATAC learns to capture the temporal dependencies of network states and coalition formation decisions over offline data, eliminating the need for expensive online interactions with large IoT networks. Given generated coalitions, MPGNN employs E-GraphSAGE to establish coalitional NIDSs which then collaborate via ensemble prediction to accomplish intrusion detection for the entire network. We evaluate MPGNN on two real-world datasets. The experimental results demonstrate the superiority of our method with substantial improvements in F1 score, surpassing the state-of-the-art methods by 0.38 and 0.29 for the respective datasets. Compared to the centralized NIDS, MPGNN reduces the training time of NIDS by 41.63% and 22.11%, while maintaining an intrusion detection performance comparable to centralized NIDS. © 2024 ACM.

关键词： Reinforcement learning

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Safety helmet detection algorithm in complex scenarios based on YOLOX

Safety helmet detection algorithm in complex scenarios based...

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2022 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2022

作者： Xiang, Dongsheng Zhu, Baisong Wu, Xu Ou, Yangfei CHENGDU UNIVERSITY of TECHNOLOGY College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China College of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu China

ISBN: (纸本)9781665462877

Aiming at the problems that the existing helmet wearing detection algorithms have many application scenarios and rich detection targets. An improved helmet detection method EPP-YOLOX based on YOLOX is proposed. Firstly, the SPP structure of YOLOX is replaced by Enhance Pyramid pooling structure to strengthen shallow feature extraction. Secondly, YOLOX multi-scale fusion structure is added, and the spatial information and semantic information extracted from the attention mechanism image are added to reduce the loss of image details Finally, the experimental results show that the average accuracy of the proposed method is as high as 93.06%, which is 3.28% higher than the original model, while the size of the model parameters is unchanged, and the detection accuracy is also greatly improved. It realizes the high-precision detection of helmet in different scenes such as weak light scene, occlusion scene and multi-target. © 2022 IEEE.

关键词： Image enhancement

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