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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Daksh Raj Rajiv Kumar Shivani Joshi Amrita Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida U.P. India Department of Computer Science & Engineering Galgotias University Greater Noida U.P. India Center for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This paper presents the critical challenge of phishing attacks by using advanced machine learning (ML) approaches to efficiently identify and mitigate malicious websites. Phishing websites betray users by impersonating legitimate entities to extract sensitive information. This paper emphases on developing and implementing a robust ML model capable of differentiating between legitimate and phishing websites through a comprehensive analysis of URL features, website content, and associated metadata. The proposed approach employs a diverse dataset that includes both phishing and legitimate websites, with features extracted using both traditional approaches and innovative methods. Various ML approaches are explored, such as Decision Trees, Support Vector Machines, Random Forest (RF), and Neural Networks. These approaches are evaluated for accuracy, recall, precision, and F1-score to determine the most effective model. Feature selection approaches are employed to discover the most significant indicators of phishing activity, improving the model's performance and interpretability. The RF Classifier demonstrated superior performance with an accuracy of 96.6%.

关键词： Uniform resource locators Support vector machines Accuracy Phishing Computational modeling Prevention and mitigation Neural networks Feature extraction Random forests Classification tree analysis

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PUF-Based Privacy-Preserving Simultaneous Authentication Among Multiple Vehicles in VANET

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IEEE Transactions on Vehicular technology 2024年第5期73卷 6727-6739页

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Akram, Muhammad Wahid Hussain, Shafiq Das, Ashok Kumar Park, Youngho Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin64002 Taiwan University of Lahore Department of Software Engineering Punjab Lahore54590 Pakistan University of Sahiwal Department of Computer Science Punjab Sahiwal57000 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Telangana Hyderabad500032 India Kyungpook National University School of Electronics Engineering Daegu41566 Korea Republic of

The intellectual and immense attainment of a smart city and auto-mobile vehicles are gradually growing. This growth drives Vehicular Ad-Hoc Network (VANET), a broadly operated complex network for inter-vehicular transmission to acquire information relevant to the vehicle location, road condition, traffic congestion, and speed. Generally, this information is procured through an open-source communication, which is susceptible to various security threats. In broad terms, the privacy of data in VANET is considered an imperative task. In this regard, many authentication protocols are formerly initiated for VANET. Most of these protocols are precarious owing to eavesdropping, replay, and impersonation attacks, etc. Furthermore, most existing protocols operates on enormous communication and computation rate, which turns them inadequate for VANET. This paper exhibits a lightweight privacy-preserving authentication protocol for n number of vehicles in VANET using the physical unclonable function (PUF) technique, and offers efficient and secure communication via public channels. Moreover, we evaluate our protocol formally and informally, showing that it is scalable, lightweight, and safe compared to formerly protocols. The rate of computation and communication is less than most related protocols. We also simulate the proposed scheme using the NS-3 simulator to measure its various network performance parameters. As a result, it is apparent through the beheld statistics that the presented protocol is beyond practical, efficient, and reliable for the secure dissemination of information in VANET. © 1967-2012 IEEE.

关键词： Vehicular ad hoc networks

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Improving Reactive Power Management and Voltage Profiles in Hybrid Renewable Energy Grids using Heterogeneous Context-Aware Graph Convolutional Network 3

Improving Reactive Power Management and Voltage Profiles in ...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Ramkumar, M. Siva Gopan, Gokul Arun, M. Giri, Jayant Al-Qawasmi, Khaled Dept of ECE SNS College of Technology India Saveetha University Chennai602105 India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Saveetha University TN Chennai602105 India Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331532420

With the growing integration of renewable energy sources (RES) into power grids (PG), managing reactive power and maintaining voltage stability have become critical challenges. This paper proposes a novel approach using a Heterogeneous Context-Aware Graph Convolutional Network (HCAGCN) to improve reactive power management and voltage profiles in hybrid renewable energy grids (REG). The objective of the proposed method is to improve the voltage profile, reduces the power loss. The proposed HCAGCN approach effectively models complex relationships among various components in hybrid REG, enabling dynamic adjustment of reactive power and enhancing overall voltage stability. By then, the proposed method is implemented in MATLAB platform and evaluated their performance with various existing methods such as Artificial Neural Network (ANN), Firefly Algorithm (FA), Kronecker Neural Network (KNN), Genetic Algorithm (GA), and Ant Lion Optimization (ALO). The HCAGCN technique achieves the lowest current THD at 0.81% and voltage THD at 3.49%, significantly outperforming the numerous existing methods. © 2024 IEEE.

关键词： Hybrid power

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Bcdnet: Balanced Coupling and Decoupling Network for Person Search

SSRN

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SSRN 2024年

作者： Lu, Zhengjie Peng, Jinjia Wang, Huibing Fu, Xianping School of Cyber Security and Computer Hebei University China Hebei Machine Vision Engineering Research Center China School of Computer Science and Technology Dalian Maritime University China

Person search aims to locate and identify individuals in unaltered scene images simultaneously. This task presents challenges in achieving collaborative optimization due to the intricate interplay between person detection and *** researches have centered on reducing the coupling between detection and re-identification. Nevertheless, some studies have indicated that neither complete decoupling nor complete coupling represents the best approach. Utilizing the intricate interplay between subtasks to balance coupling and decoupling is solving the key to the *** solve the above problems, this paper proposes a Balanced Coupling and Decoupling network (BCDnet) that decouples conflicts between multiple tasks by Multi-View Decoupling (MVD) and establishes interactions between multiple tasks by Correlation Online Instance Matching (COIM) loss. Specifically, the MVD aims to mitigate conflicts between detection and re-identification by decoupling from multiple viewpoints. At the same time, the COIM utilizes distance correlation to adjust the relationship between detection and re-identification. Combining the two leverages the intricate interplay between subtasks, balancing coupling and decoupling among different subtasks, thereby enhancing the performance of person *** framework proposed in this paper achieves the mAP of 94.5% and 56.0% on CUHK-SYSU and PRW datasets, demonstrating its effectiveness and practical utility. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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CNN Based Model for Underwater Image Classification and Enhancement

CNN Based Model for Underwater Image Classification and Enha...

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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： Sahit Jamandlamudi Deva Priya Isravel Julia Punitha Malar Dhas Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The challenge of enhancing underwater images is difficult because of several issues, including color distortion, inadequate illumination, and dispersion from suspended particles in the water. Conventional picture enhancing methods frequently find it difficult to resolve these problems. Thanks to its ability to learn intricate representations and pick up on minute features, deep learning has become a potent tool for picture improvement tasks in recent years. In this work, we suggest a novel deep learning-based method for improving underwater images. To automatically learn and improve the visual quality of underwater scenes, we use a convolutional neural network architecture that is specifically made to handle underwater photos. A sizable dataset of underwater photos is used to train the network, with an emphasis on contrast improvement and color correction. We compare our method with many state-of-the-art techniques and undertake extensive tests on a benchmark dataset to evaluate our approach. The outcomes show that our strategy performs much better than current approaches both in terms of subjective visual quality and objective criteria. Our technique improves visibility, minimizes noise, and successfully restores colors in underwater photos. By presenting a deep learning-based method that enhances the visual quality of underwater photographs, this work advances the field of underwater image processing and aids in marine research, underwater exploration, and surveillance.

关键词： Deep learning Visualization Adaptation models Image color analysis Surveillance Lighting Distortion

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Local Fault Diagnosis Analysis Based on Block Pattern of Regular Diagnosable Networks

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IEEE/ACM Transactions on Networking 2024年

作者： Lin, Limei Guan, Kaineng Huang, Yanze Hsieh, Sun-Yuan Chen, Gaolin Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

Fault diagnosability can reflect the actual self diagnosing capability of a multiprocessor system better. However, people usually focus on the overall information and neglect the important local information. In order to reflect the locality of a system at a node better, this paper proposes a novel fault diagnosis strategy, called x-block local fault diagnosability (x-BLFD), where the x-block condition requires more than x connected fault-free nodes. Then, we characterize some important properties about the x-BLFD of multiprocessors interconnected networks under the Preparata/Metze/Chien model (P/M/C), and further propose the x-BLFD in an f(x)-extended block network with the minimum (x+1)-subnetwork degree at some node. We also establish an approximate algorithm to calculate the x-BLFD of a large-scale diagnosable network at some node, and analyze the experimental performance of large-scale networks. Furthermore, we apply our proposed conclusion to obtain the x-BLFD of 16 well-known networks at some node directly under P/M/C, including dual cubes, hierarchical cubic networks, DQcubes, twisted hypercubes, Bicube networks, crossed cubes, folded hypercubes, k-ary n-cubes, balanced hypercubes, BC graphs, (n,k)-star graphs, Cayley graphs generated by transposition trees, bubble-sort star graphs, split-star networks, data center networks, and (n,k)-arrangement graphs. Finally, we compare the x-BLFD with the diagnosability, conditional diagnosability, pessimistic diagnosability, and t/k-diagnosability by a large number of detailed numerical analysis. It can be seen that the x-BLFD is greater than all the other types of fault diagnosabilities. © 1993-2012 IEEE.

关键词： Hypercube networks

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Artificial Neural Network-Based Investigation of Zirconium Diboride Nanoparticles Reinforced in an Aluminium Alloy Matrix

Artificial Neural Network-Based Investigation of Zirconium D...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Raj, P. Marshal Vennila, C. Anush Kannan, N.K. Sasikala, R. Anandaram, Harishchander Alagarsamy, Manjunathan Jai Shriram Engineering College Avinashipalayam Department of Mechanical Engineering Tamil Nadu Tiruppur638660 India Alagappa Chettiar Government College of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Karaikudi630003 India Kathir College of Engineering Department of Ece Tamilnadu Coimbatore641062 India Karpagam College of Engineering Department of Cyber Security Tamilnadu Coimbatore641032 India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India K.Ramakrishnan College of Technology Department of Electronics and Communication Engineering Tamilnadu Trichy621112 India

ISBN: (纸本)9798350383348

This study supports advanced materials by examining the properties of Al 7175 alloy reinforced with zirconium diboride (ZrB2) nanoparticles. Understanding and improving the wear resistance of composites is an important goal because it is essential for many industrial applications. Hence, in this research, Al 7175 alloy is reinforced with nanoparticles of ZrB2 at three different percentages. This research was carefully designed to investigate the connection between material (such as composition, load, speed and sliding distance) and response (such as friction force (FF), wear rate (Sf) and friction coefficient (Cf)). The optimal combinations for reducing Cf, Sf, and FF are found using Taguchi SNR analysis. The result was 5% content, 25.2 N load, 400 rpm speed, and 71.2 m drift. The confirmation test was done, and it shows that the obtained responses are optimized for the predicted optimal combinations. Also, an artificial neural network (ANN) model was developed to predict responses based on input data, another advancement that has helped data scientists. The ANN model performed well in the training, validation and testing phases, with an overall prediction rate of 87.873%. The research outcomes show that with the increase in composition percentage of the ZrB2 and the reduction in speed and load, the wear rate is minimal. Hence, the alloy Al 7175 reinforced with the nanoparticles of Zrb2 at 5% tends to reduce the wear rate and its friction coefficients. © 2024 IEEE.

关键词： Artificial Neural Network Composite materials Taguchi Wear resistance Zirconium Diboride

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EffNet-CNN:A Semantic Model for Image Mining&Content-Based Image Retrieval

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computer Modeling in engineering & Sciences 2025年第5期143卷 1971-2000页

作者： Rajendran Thanikachalam Anandhavalli Muniasamy Ashwag Alasmari Rajendran Thavasimuthu Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Informatics and Computer Systems College of Computer ScienceKing Khalid University Abha61421Saudi Arabia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia Department of Sustainable Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai602105India

Content-Based Image Retrieval(CBIR)and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare,security,and various *** image retrieval system mainly relies on the efficiency and accuracy of the classification *** research addresses the challenge of enhancing the image retrieval system by developing a novel approach,EfficientNet-Convolutional Neural Network(EffNet-CNN).The key objective of this research is to evaluate the proposed EffNet-CNN model’s performance in image classification,image mining,and *** novelty of the proposed EffNet-CNN model includes the integration of different techniques and *** model includes the Mahalanobis distance metric for feature matching,which enhances the similarity *** model extends EfficientNet architecture by incorporating additional convolutional layers,batch normalization,dropout,and pooling layers for improved hierarchical feature extraction.A systematic hyperparameter optimization using SGD,performance evaluation with three datasets,and data normalization for improving feature *** EffNet-CNN is assessed utilizing precision,accuracy,F-measure,and recall metrics across MS-COCO,CIFAR-10 and 100 *** model achieved accuracy values ranging from 90.60%to 95.90%for the MS-COCO dataset,96.8%to 98.3%for the CIFAR-10 dataset and 92.9%to 98.6%for the CIFAR-100 dataset.A validation of the EffNet-CNN model’s results with other models reveals the proposed model’s superior *** results highlight the potential of the EffNet-CNN model proposed for image classification and its usefulness in image mining and CBIR.

关键词： Image mining CBIR semantic features EffNet-CNN image retrieval

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Higher Residuosity Attacks on Small RSA Subgroup Decision Problems 28th

Higher Residuosity Attacks on Small RSA Subgroup Decision Pr...

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28th IACR International Conference on Practice and Theory of Public Key Cryptography, PKC 2025

作者： Zhao, Xiaopeng Cao, Zhenfu Dong, Xiaolei Liu, Zhusen School of Computer Science and Technology Donghua University Shanghai201620 China Department of Cryptography and Cyber Security East China Normal University Shanghai200062 China Hangzhou Innovation Institute of Beihang University Zhejiang Hangzhou311121 China

ISBN: (纸本)9783031918193

Secure two-party comparison, known as Yao’s millionaires’ problem, has been a fundamental challenge in privacy-preserving compu-tation. It enables two parties to compare their inputs without revealing the exact values of those inputs or relying on any trusted third party. One elegant approach to secure computation is based on homomorphic encryption. Recently, building on this approach, Carlton et al. (CT-RSA 2018) and Bourse et al. (CT-RSA 2020) presented novel solutions for the problem of secure integer comparison. These protocols have demon-strated significantly improved performance compared to the well-known and frequently used DGK protocol (ACISP 2007 and Int. J. Appl. Cryp-togr. 1(4),323-324, 2009). In this paper, we introduce a class of higher residuosity attacks, which can be regarded as an extension of the clas-sical quadratic residuosity attack on the decisional Diffie-Hellman prob-lem. We demonstrate that the small RSA subgroup decision problems, upon which both the CEK and BST protocols are based, are not difficult to solve when the prime base p_0 p0 is small (e.g., $ p_0 © International Association for Cryptologic Research 2025.

关键词： Number theory

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Revolutionizing Human Assessment: AI-Driven Analysis of SQ, EQ, and IQ

Revolutionizing Human Assessment: AI-Driven Analysis of SQ, ...

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cyber Resilience (ICCR), International Conference on

作者： Amer Ibrahim Wael Ali Nidal A. Al-Dmour Taher M. Ghazal American University in the Emirates Dubai United Arab Emirates Department of Computer Engineering College of Engineering Mutah University Jordan Computer Science Department Centre for Cyber Physical Systems Faculty of Information Science and Technology Khalifa University Center for Cyber Security Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This study focuses on the estimate and analysis of social quotient (SQ), emotional quotient (EQ), and intellectual quotient (IQ) to investigate the synergy between artificial intelligence (AI) and human competence evaluation. Traditional evaluation procedures confront intrinsic limitations, necessitating a deeper look at AI's ability to revolutionize existing methods. AI's ability to absorb massive quantities of data, comprehend complicated behavioral patterns, and refine the identification of social, emotional, and intellectual qualities opens the door to more nuanced evaluations. This article digs at artificial intelligence AI's crucial position in data collecting, algorithmic processing, and the ethical concerns inherent in this expanding sector. This study intends to explain the progress and consequences of altering our knowledge of human capacities across multiple dimensions by scrutinizing the transformational potential and challenges of incorporating AI into SQ, EQ, and IQ evaluations.

关键词： Ethics Behavioral sciences Appraisal Artificial intelligence Resilience

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