Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

The increasing complexity and volume of cyber threats in today’s digital era necessitate advanced detection and mitigation strategies. Traditional signature-based security systems are often inadequate against sophisticated attacks such as zero-day exploits, ransomware, and phishing campaigns. Machine Learning (ML) has emerged as a powerful tool to enhance cybersecurity measures, offering predictive, adaptive, and automated threat detection capabilities. This paper provides a comprehensive review of ML applications in cybersecurity, focusing on threat detection, intrusion detection systems (IDS), malware analysis, and anomaly detection. We discuss the advantages, limitations, and current challenges of integrating ML into cybersecurity frameworks. Moreover, the paper presents comparative analyses of various ML algorithms used in threat detection and highlights future research directions.

Keywords: Machine Learning, Cybersecurity, Threat Detection, Intrusion Detection Systems, Anomaly Detection, Malware Analysis