MD Hamid Borkot Tulla^1,*, MD Moniur Rahman Ratan², Rashid MD Mamunur³, Abdullah Hil Safi Sohan⁴, MD Matiur Rahman⁵

Journal of Cyber Security, Vol.7, pp. 675-691, 2025, DOI:10.32604/jcs.2025.074737 - 24 December 2025

Abstract Phishing is considered one of the most widespread cybercrimes due to the fact that it combines both technical and human vulnerabilities with the intention of stealing sensitive information. Traditional blacklist and heuristic-based defenses fail to detect such emerging attack patterns; hence, intelligent and transparent detection systems are needed. This paper proposes an explainable machine learning framework that integrates predictive performance with regulatory accountability. Four models were trained and tested on a balanced dataset of 10,000 URLs, comprising 5000 phishing and 5000 legitimate samples, each characterized by 48 lexical and content-based features: Decision Tree, XGBoost, Logistic… More >

Siwan Noh¹, Kyung-Hyune Rhee^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3805-3826, 2024, DOI:10.32604/cmc.2024.050949 - 20 June 2024

Abstract In Decentralized Machine Learning (DML) systems, system participants contribute their resources to assist others in developing machine learning solutions. Identifying malicious contributions in DML systems is challenging, which has led to the exploration of blockchain technology. Blockchain leverages its transparency and immutability to record the provenance and reliability of training data. However, storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational costs. Additionally, current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading data. However, less… More >

Muhammad Adnan Khan^1,2, Asma Kanwal³, Sagheer Abbas³, Faheem Khan⁴, T. Whangbo^4,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3607-3619, 2022, DOI:10.32604/cmc.2022.023480 - 07 December 2021

Abstract Cloud computing is providing IT services to its customer based on Service level agreements (SLAs). It is important for cloud service providers to provide reliable Quality of service (QoS) and to maintain SLAs accountability. Cloud service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for it. Cloud users’ major concerns; the factors for service reliability are based on response time, accessibility, availability, and speed. In this paper, we, therefore, experiment with the parallel mutant-Particle swarm optimization (PSO) for the detection and predictions of QoS violations in More >