IET Information Security最新文献_第4页

Detecting Phishing Emails Using GloVe Word Embeddings and Ensemble Machine Learning Model 使用手套词嵌入和集成机器学习模型检测钓鱼邮件

IF 2.6 4区计算机科学

IET Information Security Pub Date : 2026-03-23 DOI: 10.1049/ise2/2558485

Sulaiman A. Maeli, Ajay U. Surwade

{"title":"Detecting Phishing Emails Using GloVe Word Embeddings and Ensemble Machine Learning Model","authors":"Sulaiman A. Maeli, Ajay U. Surwade","doi":"10.1049/ise2/2558485","DOIUrl":"https://doi.org/10.1049/ise2/2558485","url":null,"abstract":"Phishing emails represent a critical cybersecurity challenge, exploiting human vulnerability to illicitly obtain sensitive personal and financial data. This study proposes a novel content-based filtering framework for phishing email detection, employing Global Vectors for Word Representation (GloVe) embeddings for semantic feature extraction and a stacked ensemble machine learning architecture for classification. Four publicly available datasets were systematically processed and amalgamated to ensure diversity and robustness in model training. The proposed hierarchical architecture integrates three distinct base learners, K-nearest neighbors (KNNs), light gradient boosting machine (LGBM), and extremely randomized trees (Extra Trees) classifier (ETC), with extreme gradient boosting (XGBoost) optimized as the meta-classifier. Comparative experimental analyses demonstrate that this ensemble model achieves superior performance relative to existing methodologies. Empirical evaluations reveal state-of-the-art metrics, including 99.84% accuracy, 99.87% precision, 99.74% recall, and a 99.81% F1-score, coupled with a minimal error rate of 0.16%. The results substantiate the framework’s efficacy in mitigating phishing threats, exhibiting statistically significant improvements in both discriminative capability and operational reliability compared to prior approaches. This advancement underscores the potential of stacked ensemble learning combined with semantic feature representation to address evolving cybersecurity vulnerabilities.","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ise2/2558485","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedAnomDetect a Clustered Federated Learning Framework for Robust Anomaly Detection With Enhanced Privacy and Communication Efficiency FedAnomDetect一种增强隐私性和通信效率的鲁棒异常检测聚类联邦学习框架

IF 2.6 4区计算机科学

IET Information Security Pub Date : 2026-03-14 DOI: 10.1049/ise2/8332879

Abeer Abdullah Alsadhan

{"title":"FedAnomDetect a Clustered Federated Learning Framework for Robust Anomaly Detection With Enhanced Privacy and Communication Efficiency","authors":"Abeer Abdullah Alsadhan","doi":"10.1049/ise2/8332879","DOIUrl":"https://doi.org/10.1049/ise2/8332879","url":null,"abstract":"Anomaly detection in distributed environments poses significant challenges, particularly in balancing privacy, communication overhead, and detection accuracy. This paper presents FedAnomDetect, a novel federated learning (FL-based framework designed for anomaly detection across large-scale, distributed systems. FedAnomDetect incorporates advanced privacy-preserving techniques, such as differential privacy, to ensure data security during the model aggregation process, while introducing adversarial robustness mechanisms to defend against model poisoning and adversarial perturbations. The framework is further optimized to reduce communication overhead through model compression and clustered FL, ensuring scalability across networks with thousands of edge devices. Experimental results demonstrate that FedAnomDetect outperforms traditional anomaly detection approaches in terms of detection accuracy, false positive rate, and communication efficiency. Additionally, we present a detailed analysis of the framework’s resilience under adversarial conditions and provide quantitative results on latency and scalability in real-world deployments. These findings validate the feasibility of FedAnomDetect for anomaly detection in large-scale distributed networks while maintaining high privacy standards and minimizing computational costs.","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ise2/8332879","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147653264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 2.6 4区计算机科学

IET Information Security Pub Date : 2026-03-14

引用次数: 0

A Novel Hybrid Image Encryption Scheme Using Henon Map for Secure Image Communication 一种基于Henon映射的混合图像加密方案

IF 2.6 4区计算机科学

IET Information Security Pub Date : 2026-02-27 DOI: 10.1049/ise2/2676559

Saba Inam, Shamsa Kanwal, Marryum Niazi, Hanen Karamti, Shaha Al-Otaibi, Mona M. Jamjoom

{"title":"A Novel Hybrid Image Encryption Scheme Using Henon Map for Secure Image Communication","authors":"Saba Inam, Shamsa Kanwal, Marryum Niazi, Hanen Karamti, Shaha Al-Otaibi, Mona M. Jamjoom","doi":"10.1049/ise2/2676559","DOIUrl":"10.1049/ise2/2676559","url":null,"abstract":"The combination of chaos theory and cryptographic methodologies constitutes a pivotal aspect of the information security. Owing to certain attributes of images, like their large data capacity and significant redundancy, image encryption needs specialized techniques instead of conventional text encryption. This study presents an innovative hybrid image encryption scheme that integrates Henon map–based key generation and involves diffusion process to ensure security and efficiency of the system. The experimental findings indicate that the methodology provides strong confusion and diffusion, low correlation between adjacent pixels, high entropy values close to 8, and number of pixel change rate (NPCR) above 99%. The method ensures secure image transmission using the same secret keys and maintains computational efficiency, making it suitable for practical applications in secure communication and multimedia protection. Unlike conventional Henon map–based encryption schemes, this study fuses Henon map–based key generation with error correction code (ECC) and an organized diffusion mechanism, enhancing both cryptographic robustness and reliability against channel errors during transmission.","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ise2/2676559","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147653372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 2.6 4区计算机科学

IET Information Security Pub Date : 2026-02-27

引用次数: 0

IF 2.6 4区计算机科学

IET Information Security Pub Date : 2026-02-23

引用次数: 0

TReS-BD: Trigger-Aware Reward Shaping for Efficient and Stealthy Backdoor Attacks in Cooperative Multi-Agent RL TReS-BD：协同多智能体强化学习中高效隐蔽后门攻击的触发感知奖励塑造

IF 2.6 4区计算机科学

IET Information Security Pub Date : 2026-02-23 DOI: 10.1049/ise2/3609361

Chaoyang Zhu, Xiao Zhu, Xiangcheng Li, Tuanfa Qin

{"title":"TReS-BD: Trigger-Aware Reward Shaping for Efficient and Stealthy Backdoor Attacks in Cooperative Multi-Agent RL","authors":"Chaoyang Zhu, Xiao Zhu, Xiangcheng Li, Tuanfa Qin","doi":"10.1049/ise2/3609361","DOIUrl":"10.1049/ise2/3609361","url":null,"abstract":"Cooperative multi-agent reinforcement learning (CMARL) has become a powerful paradigm for applications in autonomous driving, smart grids, and distributed robotics. However, its increasing adoption in safety-critical scenarios raises severe concerns about security vulnerabilities, particularly backdoor attacks, where malicious behaviors are embedded and activated only under specific triggers. This paper presents TReS-BD, a trigger-aware reward shaping framework that systematically exploits CMARL vulnerabilities at the reward level. By formulating backdoor manipulation as a Bayes-Adaptive Markov Decision Process (BAMDP), TReS-BD treats trigger conditions as latent variables and integrates KL-regularized policy optimization to maintain nominal task performance while inducing adversarial behaviors under triggers. Extensive experiments on standard benchmarks, including multi-agent particle environments (MPE) and StarCraft Multi-Agent Challenge (SMAC), with representative algorithms MADDPG and MAPPO, demonstrate that TReS-BD achieves over 90% attack success with less than 1% training data poisoning, while maintaining normal performance and evading current detection mechanisms. Furthermore, a systematic analysis of existing defenses reveals their ineffectiveness against such reward-level attacks, underscoring a critical blind spot in current CMARL security frameworks. Our findings highlight the urgent need for robust defense strategies and provide new insights into safeguarding cooperative multi-agent systems against stealthy and efficient backdoor threats.","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ise2/3609361","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147653394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing E-Commerce Security Based on Involution Function Using Twin Key AES Cipher 基于对合函数的双密钥AES密码增强电子商务安全性

IF 2.6 4区计算机科学

IET Information Security Pub Date : 2026-02-17 DOI: 10.1049/ise2/5710384

Biresh Kumar, Anurag Sinha, Saroj Kumar Pandey, Rishav Dubey, Shubham Shukla, Ahmed Alkhayyat, Sharmistha Roy, Md. Sazid Reza, Ayodele Lasisi, Quadri Noorulhasan Naveed, Ali M. Aseere

{"title":"Enhancing E-Commerce Security Based on Involution Function Using Twin Key AES Cipher","authors":"Biresh Kumar, Anurag Sinha, Saroj Kumar Pandey, Rishav Dubey, Shubham Shukla, Ahmed Alkhayyat, Sharmistha Roy, Md. Sazid Reza, Ayodele Lasisi, Quadri Noorulhasan Naveed, Ali M. Aseere","doi":"10.1049/ise2/5710384","DOIUrl":"https://doi.org/10.1049/ise2/5710384","url":null,"abstract":"In the realm of surging e-commerce data and increasing digital transactions, ensuring end-to-end security is paramount to protect sensitive information and maintain user trust. The Advanced Encryption Standard (AES), a cornerstone of network security, relies on symmetric key encryption for secure communication. However, rapid advancements in cryptanalysis and computational power pose new threats to AES, particularly exposing vulnerabilities related to its static S-Box design. This article is motivated by the urgent need to enhance cryptographic resilience in dynamic threat environments, especially for applications involving online financial systems, secure cloud storage, and real-time communication. To address these concerns, we propose an involution-based concurrent error detection (CED) mechanism for dynamic twin-key generation, significantly improving resistance against differential and linear cryptanalysis. By exploring the evolution of involution structures in stream cipher construction, we introduce a low-latency CED framework tailored for symmetric block ciphers. The proposed method outperforms traditional AES-128 by leveraging a dual-key encryption approach. Additionally, we design a programmable, key-dependent S-Box architecture that enhances unpredictability and fortifies the encryption process, particularly in hardware-constrained environments such as IoT devices and embedded systems. Mathematical analysis substantiates the robustness and practicality of the proposed enhancements, offering a future-ready solution to secure modern data systems against emerging decryption techniques.","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ise2/5710384","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147320768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 2.6 4区计算机科学

IET Information Security Pub Date : 2026-02-17

引用次数: 0

IF 2.6 4区计算机科学

IET Information Security Pub Date : 2026-02-16

引用次数: 0