Machine Learning Applications in Cyber Threat Intelligence: A Comprehensive Review

Abstract

Contemporary institutions are consistently confronted with fraudulent activities that exploit weaknesses in interconnected systems. Securing critical data against unauthorized access by hackers and other cybercriminals requires the application of robust cybersecurity protocols. As the number and complexity of cyber threats continue to grow, innovative prevention strategies are required. The objective of this study is to investigate the correlation between machine learning (ML) and cyber threat intelligence (CTI) to improve cybersecurity strategies. For the detection of anomalies, the analysis of malware, and the prediction of threats, ML techniques are indispensable in industries including retail, finance, healthcare, and cybersecurity. By employing critical threat information (CTI), security teams can gain a comprehensive understanding of adversary strategies and bolster defensive measures; thus, they play a pivotal role in proactive defense. Integration of ML and CTI facilitates exhaustive analysis by automating the acquisition, processing, and categorization of data. However, obstacles arise when confronted with issues such as risk assessment, the requirement for precise data, and the initial stages of machine learning implementation in business intelligence. In this paper, we present an extensive examination of the current literature concerning the visualization of Cyber Threat Intelligence (CTI) and the utilization of Machine Learning (ML. Therefore, the report concludes with an analysis of emergent threats, potential future applications of AI and ML in the field of cyber threat intelligence, and the critical contribution of machine learning to the improvement of cybersecurity.