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Cyber security and Applications 2024年 2卷

作者： Vajrobol, Vajratiya Gupta, Brij B. Gaurav, Akshat Institute of Informatics and Communication University of Delhi India International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Ronin Institute Montclair NJ United States Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut 1102 Lebanon Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India

Phishing is a cybersecurity problem that hackers employ to deceive individuals and organizations. Phishing is dynamic in nature;the hackers change several tricks to deceive the victims in multiple ways. It is important to track the tricks of hackers with recent technology. This study makes a notable contribution to enhancing cybersecurity defences by offering insights that aid in the detection and mitigation of phishing threats. Specifically, the study's analysis of URLs using mutual information and logistic regression techniques yielded a remarkably high accuracy rate of 99.97%, surpassing previous efforts. The identification of the most informative features for distinguishing phishing attempts provides valuable intelligence for cybersecurity professionals, enabling them to bolster defenses and stay ahead of evolving phishing tactics. © 2024

关键词： Feature selection Logistic regression Mutual information Phishing URL analysis

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Secure and sustainable food processing supply chain framework based on Hyperledger Fabric technology

Cyber Security and Applications

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Cyber security and Applications 2024年 2卷

作者： Rahaman, Mosiur Tabassum, Farhin Arya, Varsha Bansal, Ritika International Center for AI and Cyber Security Research and Innovations (CCRI) & Department of Computer Science and Information Engineering Asia University Taiwan China Indian Institute of Technology Kharagpur India Department of Business Administration Asia University Taiwan China Department of Electrical and Computer Engineering Lebanese American University Beirut 1102 Lebanon Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Insights2Techinfo India UCRD Chandigarh University Chandigarh India

Food processing supply chains are gradually facing the problem of incorporation and sustainability because of the complexity of many participants involved in the supply chain network. Customers are very aware of and particularly interested in the quality, safety, and provenance of food processing products. However, conventional supply chains, on the other hand, rely heavily on a third party for transactions and confidence. Traditional supply chain models only partially reveal information about an organization to other parties, which results in inadequate data and a communication gap. Although emails and printed papers offer some information, the capacity to provide utterly accurate visibility and traceability information is impossible since the items throughout the supply chain are difficult to trace. In this research, we provide a fully distributed method, Hyperledger Fabric, to establish a food processing supply chain system incorporating a Hyperledger Fabric framework designed to demonstrate the efficiency of the approach and analyze the main use cases needed in a food processing supply chain network. © 2024

关键词： Hyperledger fabric security Supply chain Sustainability

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Quasi-Newton optimised Kolmogorov-Arnold Networks for wind farm power prediction

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Heliyon 2024年第23期10卷 e40799页

作者： Mubarak, Auwalu Saleh Ameen, Zubaida Said Mati, Sagiru Lasisi, Ayodele Naveed, Quadri Noorulhasan Abdulkadir, Rabiu Aliyu Operational Research Centre in Healthcare Near East University TRNC Mersin 10 Nicosia 99138 Turkey Department of Electrical Engineering Aliko Dangote University of Science and Technology Kano Wudil Nigeria Department of Biochemistry Northwest University Kano Nigeria Department of Economics Northwest University Kano Nigeria Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Mechatronics Aliko Dangote University of Science and Technology Kano Nigeria Center for Cyber Security Faculty of Information Science and Technology UKM BANGI Malaysia

Having accurate and effective wind energy forecasting that can be easily incorporated into smart networks is important. Appropriate planning and energy generation predictions are necessary for these infrastructures. The production of wind energy is linked to instability and unpredictability. Wind energy forecasting has traditionally been performed using statistical models, but with the advent of artificial intelligence (AI), research indicates that AI is more accurate than the statical technique. In this study, the nominal power of six wind farms in China was predicted using Kolmogorov-Arnold Networks (KAN) and Multilayer Perceptron (MLP) models. KAN as an alternative to the conventional MLP, has the ability to handle problems with scalability, vanishing gradients, and interpretability associated with MLP. The KAN uses learnable B-Spline as activation functions prompting it to address the issues of the MLP. We employed the Radial Basis Function (RBF) with Gaussian kernels to approximate the 3-order B-spline basis. In most deep learning models stochastic gradient-based optimization algorithms such as Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent (SGD) optimizer are mostly employed, a quasi-Newton optimization technique Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm LBFGS was employed in this work to approximate the Hessian matrix and estimate the parameter space's curvature. Also, in the preprocessing of the data, the Interquartile Range (IQR) technique is used to handle outliers and a clustering-based K-Nearest Neighbor (KNN) imputer to handle missing values. Based on different sites, the KAN-LBFGS shows superior performance based on the performance evaluation metrics with site 5 achieving MSE of 0.0039, RMSE of 0.0622, MAE of 0.0352, and DC of 0.9468. The study highlights the importance of the model's architecture, preprocessing and optimization techniques. © 2024 The Author(s)

关键词： KAN LBFGS MLP Sustainability Wind

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A Tutorial on Movable Antennas for Wireless Networks

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IEEE Communications Surveys and Tutorials 2025年

作者： Zhu, Lipeng Ma, Wenyan Mei, Weidong Zeng, Yong Wu, Qingqing Ning, Boyu Xiao, Zhenyu Shao, Xiaodan Zhang, Jun Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore National Key Laboratory of Wireless Communications Chengdu611731 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Beihang University School of Electronic and Information Engineering Beijing100191 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Beijing Institute of Technology State Key Laboratory of CNS/ATM Miit Key Laboratory of Complex-field Intelligent Sensing Beijing100081 China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed. We also highlight promising research directions in this area to inspire future investigatio

关键词： Antenna arrays

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Access Control Based on Self-Sovereign Identity

Access Control Based on Self-Sovereign Identity

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Stefania Loredana Nita Marius Iulian Mihailescu Valentina Marascu Marius Rogobete Department of Computers and Cybersecurity Faculty of Information Systems and Cyber Security Military Technical Academy “Ferdinand I” Bucharest Romania Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science “Spiru Haret” University Bucharest Romania Low Temperature Plasma Department National Institute for Laser Plasma and Radiation Physics Bucharest Romania Telematics Harman International Bucharest Romania

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

In the rapidly growing interest in digital identity management, there is an important shift toward adopting verifiable credentials, often based on blockchain technology. This transition highlights a growing interest in the concept of Self-Sovereign Identity (SSI), a paradigm that empowers individuals with control over their own digital identities through the use of verifiable credentials. This paper introduces a novel access control framework that leverages the principles of SelfSovereign Identity to significantly enhance the security of system access based on the users’ credentials. By integrating the decentralized and user-centric nature of SSI, our proposed framework aims to address existing vulnerabilities in traditional access control systems. It provides a robust, scalable, and controlled mechanism that ensures higher levels of trust and security in digital interactions. This approach aligns with the broader digital identity trends favoring transparency, user autonomy, and trust.

关键词： Access control computers Privacy Computational modeling computer architecture Market research Blockchains

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EEG based Parkinson Detection through Supervised information Enhanced Contrastive Learning

EEG based Parkinson Detection through Supervised Information...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jian Song Xiang Li Wenjing Jiang Chunxiao Wang Zhigang Zhao Jialiang Lv Bin Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Geriatric Neurology Key Laboratory of Cardiovascular Proteomics of Shandong Province Qilu Hospital of Shandong University Jinan China Institute of Engineering Medicine Beijing Institute of Technology Beijing China

This study presents a novel Supervised information Enhanced Contrastive Learning Algorithm for Parkinson’s Disease Detection (SI-CLAPD) based on Electroencephalography (EEG). SI-CLAPD performs contrastive learning in a multi-granularity manner on enhanced contextual views to achieve robust contextual representations for EEG, thus could contribute to improving the effectiveness of PD detection. Unlike existing methods for constructing PD detection models guided solely by classification loss, we propose a joint learning model that combines self-supervised contrastive learning with supervised classification learning. This model is optimized using both contrastive loss and classification loss, allowing it to capture subtle differences between EEG signals and representations that are specific to PD detection. Through extensive experimental evaluations, we demonstrate that SI-CLAPD achieves robust and high accuracy in PD detection tasks on three benchmark datasets. To the best of our knowledge, this study represents the first effort in validating the effectiveness of contrastive learning for the detection of PD. Besides, within the realm of contrastive learning research in EEG, it also represents the first endeavor to fuse supervised learning with self-supervised contrastive learning for EEG classification. This investigation unveils an universally applicable approach to EEG signal processing, with the potential to confer advantages to a multitude of EEG classification tasks.

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人类泛基因组参考图谱发布开启基因组学领域的新篇章

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science Bulletin 2023年第14期68卷 1484-1487页

作者：王博党宁馨杨晓飞徐书华叶凯 School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China State Key Laboratory of Genetic Engineering Human Phenome InstituteZhangjiang Fudan International Innovation CenterCenter for Evolutionary BiologySchool of Life SciencesFudan UniversityShanghai 200032China Ministry of Education Key Laboratory of Contemporary Anthropology Collaborative Innovation Center for Genetics and DevelopmentFudan UniversityShanghai 200032China Department of Liver Surgery and Transplantation Liver Cancer InstituteZhongshan HospitalFudan UniversityShanghai 200032China School of Life Science and Technology Shanghai Tech UniversityShanghai 201210China

The significance of DNA sequencing in advancing medicalresearch,improving healthcare practices,and enhancing ourunderstanding of human evolution cannot be *** yearmarks the 20th anniversary of the completion of the Human Genome Project(HGP)in 2003,with the draft human genome reference published in 2001[1,2].However,even with the latesthuman reference genome,GRCh38,there remain gaps totalingapprox.150 Mb,involving complex sequence in the pericentromeric and subtelomeric regions,as well as recent segmentalduplications and both ampliconic gene and ribosomal DNA(rDNA)arrays[3].A groundbreaking achievement was recently accomplished by the Telomere-to-Telomere(T2T)consortium whosuccessfully closed the remaining gaps and completed the firstend-to-end assembly of a human haploid genome,known as T2TCHM13[3].

关键词：基因组学 breaking accomplished

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A Graph Convolution Neural Network Based Method for Insider Threat Detection

A Graph Convolution Neural Network Based Method for Insider ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Kexiong Fei Jiang Zhou Lin Su Weiping Wang Yong Chen Fan Zhang School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Computer Science Texas Tech University Lubbock USA Information Technology Center China Mobile Beijing China

In this research, we propose Log2Graph, a new insider threat detection method based on graph convolution neural network (GCN). This method first retrieves the corresponding logs and features from log files through feature extraction. Specifically, we use an auxiliary feature of anomaly index to describe relationship between entities, such as users and hosts, instead of establish complex connections between them. Second, these logs and features are augmented through a combination of oversampling and downsampling, to prepare for the next-stage supervised learning process. Third, we use three elaborated rules to construct the graph of each user by connecting the logs according to chronological and logical relationship. At last, the graph convolution neural network constructed is used to detect insider threats. Our validation and evaluation results confirm that Log2Graph can greatly improve the performance of detecting insider threats compared against baseline and existing methods.

关键词： Knowledge engineering Convolution Neural networks Supervised learning Feature extraction Time measurement Data models

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Design of a Secure Machine Learning-Based Malware Detection and Analysis Scheme

Design of a Secure Machine Learning-Based Malware Detection ...

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Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Pankaj Kumar Mohammad S. Obaidat Piyush Pandey Mohammad Wazid Ashok Kumar Das Devesh Pratap Singh Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India King Abdullah II School of Information Technology The University of Jordan Amman Jordan School of Computing SRM University Jordan University of Science and Technology Beijing Beijing China School of Computing SRM University Kattankulathur TN India Center for Security Theory Algorithmic Research International Institute of Information Technology Hyderabad India

Today's world is rapidly growing towards the advanced concept of technology. Technology nowadays is at its peak of development and has great advantages in all fields of work, whether it is in industrial automation, farming, weather forecasting, or disease detection and control. Despite its great advantages, it does have its negative side because a major population is using technology and general-purpose computing devices (i.e., desktops, laptops, smartphones, tablets, etc.). Therefore, most of their personal data is available online. The data is constantly in danger of getting stolen by malicious actors (i.e., hackers), who can find a way to get their hands on this data and then use it for their personal gain (i.e., money-making). The best-known technique to get this information is by using malware exploitation on the machine of the target. Malware is an executable or a binary that is malicious in nature. It may have various devastating effects, and in order to detect and make the system defensive, we need to propose a scheme to detect the malware in the system, analyze the particular file, and then mitigate it. In this paper, we propose a secure machine learning-based malware detection and analysis scheme (in short, SML-MDA). The conducted security analysis and formal security verification of the proposed SMLMDA prove its security against various types of potential attacks. The proposed SML-MDA is then compared with the other similar competing schemes, and its performance has been found to be better in terms of various performance metrics like accuracy and F1-score.

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2024年 896卷 v-vi页

作者： Roy, Nihar Ranjan Tanwar, Sudeep Batra, Usha Center for Cyber Security and Cryptology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science Engineering Shri Vishwakarma Skill University Haryana Gurugram India Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad India

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