REVIEW: ARTIFICIAL INTELLIGENCE BASED CYBER THREAT DETECTION INCORPORATING MACHINE LEARNING ALGORITHM
DOI: https://doi.org/10.65725/JCISE/2/2/003
JOURNAL OF COMPUTATIONAL INTELLIGENCE SCIENCE AND ENGINEERING (JCISE)
ISSN: 3107-8168
Volume 2 Issue 2, Apr-Jun 2026

Abstract: Cybersecurity threats have grown more complex and frequent, creating serious risks for organizations, critical infrastructure, and individuals worldwide. Traditional signature-based security tools can no longer effectively identify and deal with advanced, evasive, and quickly changing cyber-attacks, such as zero-day exploits, ransomware, and multi-stage intrusion campaigns. As a result, there is a strong need for real-time cyber threat detection and response systems that adjust dynamically and offer timely, actionable information to security operations teams. This paper provides a detailed review of modern methods that combine machine learning (ML) and open-source intelligence (OSINT) gathered through automated web data scraping. Machine learning offers powerful analysis for spotting both known and unknown threats by recognizing patterns and detecting anomalies in various telemetry data, including network traffic, system logs, and endpoint activities. OSINT enhances these systems by supplying external insights into new vulnerabilities, threat actor tactics, techniques, and procedures (TTPs), as well as real-time cyber threat intelligence shared across open channels like social media, security forums, paste sites, and the dark web[1][2][3].By combining ML-based internal monitoring with continuously updated OSINT feeds, advanced systems improve threat classification accuracy, lower false alarms, and provide contextual information that aids proactive responses. This review looks into the key architectures, machine learning algorithms, and natural language processing techniques for analyzing OSINT, along with illustrative case studies in IoT, finance, and healthcare. It also highlights existing challenges, such as managing data quality, ensuring model robustness, and addressing privacy and compliance issues. It outlines future research directions, focusing on federated learning, explainability, and blockchain-enabled threat intelligence sharing. This paper aims to be a valuable resource for researchers and practitioners seeking more effective, adaptable, and integrated cyber security defence frameworks that can tackle the increasingly sophisticated threat landscape.

Authors: Spoorthi B S, Namana D C, Rohan K and Amrutha H P

Keywords: Cybersecurity, Machine Learning (ML) and Open-source Intelligence (OSINT).