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Comprehensive Analysis of Machine Learning Approaches for Phishing Website Detection

Comprehensive Analysis of Machine Learning Approaches for Ph...

Cognitive computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Pragna K P Suhani Shetty Ashwini Kodipalli Ushasree. A Shridhar B. Devamane Roshan Joy Martis Dept. of Electronics and Communication Engineering Global Academy Of Technology Bengaluru Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bengaluru Karnataka India Dept. of Computer Science & Engineering Gokaraju Lailavathi Engineering College Hyderabad India Department of Computer Science School of Computing Amrita Vishwavidyapeetam Mysuru Dept. of Electronics & Communication Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Website phishing, a notable cybercrime, involves deceiving individuals by impersonating legitimate websites. URL spoofing, a common technique in website phishing, involves creating website names. The primary objective of this paper is to accurately differentiate between legitimate and phishing sites using relevant URL attributes and different binary classification machine learning algorithms. This study compares simple, bagging, and boosting algorithms across performance analysis metrics. The best-performing models are selected for an ensemble approach. Additionally, various hyperparameter tuning techniques are assessed for the Random Forest Classifier, and Explainable AI methods like LIME and SHAP are used to interpret findings. The experiment yields optimal parameters, achieving 89. % accuracy with an ensemble model with K-Nearest Neighbors, Decision Tree, XG Boost, and Cat Boost. These findings highlight the significance of ensemble models and hyperparameter tuning in machine learning.

关键词： Uniform resource locators Machine learning algorithms Explainable AI Phishing Computational modeling Biological system modeling Nearest neighbor methods Performance analysis Tuning Random forests

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Reducing the clustering challenge in the IoT using two disjoint convex hulls

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Scientific Reports 2025年第1期15卷 1-10页

作者： Li, Huxiong Sadeghi Bigham, Bahram Gheisari, Mehdi Karamoozian, Aminreza Sun, Panjun Wan, Yi Institute of Artificial Intelligence Shaoxing University Zhejiang China Department of Computer Science Faculty of Mathematical Sciences Alzahra University Tehran Iran Department of computer science and Engineering Saveetha School of engineering Saveetha Institute of Medical and Technical Science Tamil Nadu India Department of Computer Engineering Shiraz Branch Islamic Azad University Shiraz Iran Department of R & D Shenzhen BKD Co LTD Shenzhen China Centre for Augmented Intelligence and Data Science School of Computing University of South Africa Johannesburg South Africa

Accurate clustering of IoT devices is a promising challenge. We have observed that a few studies have been performed to address this challenge. However, they are expensive or do not shape accurate clustering. To fill this gap, in this study, we first solve a geometric version of a big challenge in pure mathematics: the NP-hard “Almost ” problem. Then, we solve it in a polynomial time. To clarify the concept, we present it as the “Two Disjoint Convex Hulls” challenge. We solve this challenge using two algorithms: the first is “Naive” and the second is faster than the “Naive” one can solve it in polynomial order,. In addition to providing a mathematical proof of our solution, we demonstrate its superior performance within an IoT industrial ecosystem. © The Author(s) 2025.

关键词： Algorithm Binary sensor network Computational geometry Convex hull SAT Separation axis theorem

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ICGNN: Graph Neural Network Enabled Scalable Beamforming for MISO Interference Channels

arXiv

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arXiv 2025年

作者： He, Changpeng Lu, Yang Ai, Bo Dobre, Octavia A. Ding, Zhiguo Niyato, Dusit School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Faculty of Engineering and Applied Science Memorial University St. John’sNLA1C 5S7 Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a quality-of-service constrained energy efficiency maximization problem. The ICGNN is two-stage, where the direction and power parts of beamforming vectors are learned separately but trained jointly via unsupervised learning. By formulating the dimensionality of features independent of the transceiver pairs, the ICGNN is scalable with the number of transceiver pairs. Besides, to improve the performance of the ICGNN, the hybrid maximum ratio transmission and zero-forcing scheme reduces the output ports, the feature enhancement module unifies the two types of links into one type, the subgraph representation enhances the message passing efficiency, and the multi-head attention and residual connection facilitate the feature extracting. Furthermore, we present the over-the-air distributed implementation of the ICGNN. Ablation studies validate the effectiveness of key components in the ICGNN. Numerical results also demonstrate the capability of ICGNN in achieving near-optimal performance with an average inference time less than 0.1 ms. The scalability of ICGNN for unseen problem sizes is evaluated and enhanced by transfer learning with limited fine-tuning cost. The results of the centralized and distributed implementations of ICGNN are illustrated. © 2025, CC BY.

关键词： Graph neural networks

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Movable Antenna Aided Multiuser Communications: Antenna Position Optimization Based on Statistical Channel Information

arXiv

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arXiv 2025年

作者： Yan, Ge Zhu, Lipeng Zhang, Rui The NUS Graduate School National University of Singapore Singapore119077 Singapore The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

The movable antenna (MA) technology has attracted great attention recently due to its promising capability in improving wireless channel conditions by flexibly adjusting antenna positions. To reap maximal performance gains of MA systems, existing works mainly focus on MA position optimization to cater to the instantaneous channel state information (CSI). However, the resulting real-time antenna movement may face challenges in practical implementation due to the additional time overhead and energy consumption required, especially in fast time-varying channel scenarios. To address this issue, we propose in this paper a new approach to optimize the MA positions based on the users’ statistical CSI over a large timescale. In particular, we propose a general field response based statistical channel model to characterize the random channel variations caused by the local movement of users. Based on this model, a two-timescale optimization problem is formulated to maximize the ergodic sum rate of multiple users, where the precoding matrix and the positions of MAs at the base station (BS) are optimized based on the instantaneous and statistical CSI, respectively. To solve this non-convex optimization problem, a log-barrier penalized gradient ascent algorithm is developed to optimize the MA positions, where two methods are proposed to approximate the ergodic sum rate and its gradients with different complexities. Finally, we present simulation results to evaluate the performance of the proposed design and algorithms based on practical channels generated by ray-tracing. The results verify the performance advantages of MA systems compared to their fixed-position antenna (FPA) counterparts in terms of long-term rate improvement, especially for scenarios with more diverse channel power distributions in the angular domain. © 2025, CC BY.

关键词： Convex optimization

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Accountable Many-to-One Signature with Short Verification Key for Self-Sovereign Identity

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Yu, Yong Yang, Haochen Li, Yannan Du, Xiaojiang Shaanxi Normal University School of Computer Science Xi'an710119 China University of Wollongong School of Computing and Information Technology WollongongNSW2522 Australia Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Self-Sovereign Identity (SSI) shifts identity management authority from central institutions to users, enhancing privacy protections. However, malicious identity providers may collude with users to issue credentials that pass verification but contain false information. While multi-signature schemes enable joint credential issuance by multiple identity providers to mitigate this risk, they result in a linear increase in the size of signatures within credentials and verification keys as the number of identity providers grows, leading to substantial storage overhead for both users and verifiers. Furthermore, malicious verifiers may leak users' credentials to third parties or unlawfully duplicate them, causing users to lose control over the distribution of their credentials. To address these challenges, we propose the Accountable Many-to-One Signature Scheme with Short Verification Key (ASVK-MOSS), a multi-signature scheme with designated verifiers and accountability that ensures fixed sizes for both the verification key and the signature. We prove the security of the proposed ASVK-MOSS under the random oracle model. Building on ASVK-MOSS, we design a novel SSI system, named MO-SSI, in which multiple identity providers jointly sign the user's personal information to generate a credential with fixed sizes. Service providers only need to store a minimal, fixed-size verification key to validate the credentials from any set of identity providers. Additionally, the designated verifier and accountability mitigate risks posed by malicious service providers and identity providers within MO-SSI. The theoretical analysis and experimental results demonstrate its effectiveness and feasibility. © 2004-2012 IEEE.

关键词： Authentication

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Deep Learning-based Mango Leaf Disease Detection using Yolov8

Deep Learning-based Mango Leaf Disease Detection using Yolov...

引用

Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Gladson Oliver S Prathusha Perugu Jeevitha S Rashima Mahajan Jasmine Gnana Malar A Raghavan Sheeja AhilanAppathurai Aarti Information Technology Government College of Technology Coimbatore India Department of Computer Science Engineering Sri Venkateswara College of Engineering Tirupathi India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Manav Rachna International Institute of Research and Studies Faridabad India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India Artificial Intelligence and Data Science R.M.K Engineering College Gummidipoondi Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tirunelveli Tamilnadu India School of Computer Science and Engineering Geeta University Panipat

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The mango holds a significant position in India's fruit crop rotation and plays a major part in the nation's economy. The best strategy to avoid losing money and time is to recognize diseases in crops earlier. Obtaining a comprehensive dataset can be challenging and time-consuming. Additionally, deep learning models may struggle with detecting early-stage symptoms of diseases. In this work, a novel deep learning-based model is proposed for the early detection of mango leaf disease (MLD). An adaptive unsharp mask-guided (AUG) filter is used as a pre-processing step to improve the input pictures from the collected dataset. The enhanced images are fed into the YOLOV8 model for detecting mango leaf disease (MLD) and classifying the different kinds of disease. According to the experimental result, the proposed model attains a 99.29% success rate for the classification of disease in mango leaf. The proposed approach is compared to DL methods such as SSD, Mask RCNN, and YOLO v8. The detection accuracy is improved by the YOLO network over SSD, Mask R-CNN, and Yolo v8 by 7.57%, 4.89%, and 2.36%, respectively. In comparison to SVM, ANN, and CNN-HOG, the proposed approach advances the overall accuracy range by 4.72%, 0.73%, 13.18%, and 5.44%, respectively.

关键词： YOLO Support vector machines Deep learning Adaptation models Accuracy Biological system modeling Neural networks Crops Convolutional neural networks Diseases

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Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge computing Networks

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IEEE Transactions on Mobile computing 2025年

作者： Tang, Jian Li, Xiuhua Li, Hui Li, Penghua Wang, Xiaofei Leung, Victor C.M. School of Big Data & Software Engineering Chongqing University Chongqing400000 China School of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China College of Intelligence and Computing Tianjin University 300072 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T1Z4 Canada

Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs), client selection and bandwidth allocation is key for achieving cost-efficient FL in MEC networks with limited bandwidth. To address these challenges, we investigate the issue of joint client selection and bandwidth allocation for reducing the cost (i.e., latency and energy consumption) of FL training. We formulate the problem and decompose it into a holistic subproblem to reduce the number of rounds and a partial subproblem to reduce the costs of FL each round. We propose a joint class-balanced client selection and bandwidth allocation (CBCSBA) framework to address the whole problem. Specifically, for the holistic subproblem, CBCSBA combines MCs into groups, each having data distribution as close as possible to class-balanced distribution;For the partial subproblem, CBCSBA reduces costs by exploratively selecting a group and sequentially optimizing the latency and energy consumption of MCs within the group. Experimental results show that CBCSBA outperforms the baseline frameworks in reducing latency by 28.2% and energy consumption by 25.3% on average in the considered four datasets. Our code is available here. © 2002-2012 IEEE All rights reserved.

关键词： Federated learning

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A transactional-behavior-based hierarchical gated network for credit card fraud detection

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Xie, Yu Zhou, MengChu Liu, Guanjun Wei, Lifei Zhu, Honghao De Meo, Pasquale College of Information Engineering Shanghai Maritime University Shanghai201306 China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science Tongji University Shanghai201804 China College of Computer Science and Information Engineering Bengbu University Bengbu233030 China Department of Ancient and Modern Civilizations University of Messina Messina98166 Italy

The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit card companies have used rule-based approaches to detect fraudulent transactions, but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms. Despite significant progress, the current approaches to fraud detection suffer from a number of limitations: for example, it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions, and they often neglect possible correlations among transactions, even though they could reveal illicit behaviour. In this paper, we propose a novel credit card fraud detection (CCFD) method based on a transaction behaviour-based hierarchical gated network. First, we introduce a feature-oriented extraction module capable of identifying key features from original transactions, and such analysis is effective in revealing the behavioural characteristics of fraudsters. Second, we design a transaction-oriented extraction module capable of capturing the correlation between users' historical and current transactional behaviour. Such information is crucial for revealing users' sequential behaviour patterns. Our approach, called transactional-behaviour-based hierarchical gated network model (TbHGN), extracts two types of new transactional features, which are then combined in a feature interaction module to learn the final transactional representations used for CCFD. We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42% and 6.53% and an improvement in average AUC between 0.63% and 2.78% over the state of the art. © 2025 institute of electrical and Electronics Engineers Inc.. All right

关键词： Deep learning

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RAINER: A Robust Ensemble Learning Grid Search-Tuned Framework for Rainfall Patterns Prediction

arXiv

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arXiv 2025年

作者： Li, Zhenqi Zhong, Junhao Wang, Hewei Xu, Jinfeng Li, Yijie You, Jinjiang Zhang, Jiayi Wu, Runzhi Dev, Soumyabrata The Fu Foundation School of Engineering and Applied Science Columbia University United States Biomedical Engineering Department Pratt School of Engineering Duke University United States School of Computer Science University College Dublin Ireland Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Robotics Institute School of Computer Science Carnegie Mellon University United States Faculty of Science and Engineering University of Nottingham Ningbo China China College of Intelligence and Computing Tianjin University China The ADAPT SFI Research Centre

Rainfall prediction remains a persistent challenge due to the highly nonlinear and complex nature of meteorological data. Existing approaches lack systematic utilization of grid search for optimal hyperparameter tuning, relying instead on heuristic or manual selection, frequently resulting in sub-optimal results. Additionally, these methods rarely incorporate newly constructed meteorological features such as differences between temperature and humidity, to capture critical weather dynamics. Furthermore, there is a lack of systematic evaluation of ensemble learning techniques and limited exploration of diverse advanced models introduced in the past one or two years. To address these limitations, we propose a Robust ensemble LeArning grId search-tuNed framEwoRk (RAINER) for rainfall prediction. RAINER incorporates a comprehensive feature engineering pipeline, including outlier removal, imputation of missing values, feature reconstruction, and dimensionality reduction via Principal Component Analysis (PCA). The framework integrates novel meteorological features to capture dynamic weather patterns and systematically evaluates non-learning mathematical-based methods and a variety of machine learning models, from weak classifiers (e.g., KNN, LASSO, and Random Forest) to advanced neural networks such as Kolmogorov-Arnold Networks (KAN). By leveraging grid search for hyperparameter tuning and ensemble voting techniques, RAINER achieves state-of-the-art results within real-world dataset. Extensive experiments demonstrate the framework’s superior performance across diverse metrics, such as Accuracy, Precision, Recall, F1-score, and AUC, highlighting the pivotal role of feature engineering, grid search, and ensemble learning in rainfall prediction tasks. © 2025, CC BY.

关键词： Dimensionality reduction

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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