Application of AI and ML in Threat Detection
Oviya Marimuthu, Priyadharshini Ravi, Senthil Janarthanan · 2025
Modern technologies are needed to detect and efficiently combat the increasingly complex threats that are emerging in the ever-evolving sector of cybersecurity. This book chapter looks in-depth at how machine learning (ML) and artificial intelligence (AI) can be used to improve the systems for identifying and countering these sophisticated threats. The chapter examines the dynamic confluence of cybersecurity, artificial intelligence, and machine learning. It offers insightful information about how these technologies might significantly alter threat defense tactics. This involves putting advanced threats—which can range from sophisticated malware and ransomware to targeted and persistent attacks—into context and highlighting their widespread nature. In order to keep ahead of changing threat landscapes, it highlights the necessity of proactive defense measures that make use of AI and ML's adaptive and learning capabilities. The investigation is set up to give readers a comprehensive grasp of the problems that advanced threats present and how AI and ML are essential tools for solving them. The story also delves into the domain of deep learning models, specifically neural networks, emphasizing their capacity to self-adapt to new cyberthreats, making them important in threat detection. The debate is around the practical implications of real-time threat intelligence and incident response, highlighting the critical role that AI and ML play in improving the speed and accuracy of cyber security processes. Proactive action lowers the likelihood of successful cyberattacks by a large margin. The chapter weaves together ethical issues and the difficulties of using AI and ML into threat detection techniques. The conversation highlights the importance of responsible AI practices by discussing issues like algorithmic bias, the interpretability of judgments made by AI, and the general requirement for accountability and transparency when utilizing these technologies in the cybersecurity space. A number of case studies and examples illustrate the usefulness and effectiveness of AI and ML applications in a variety of threat scenarios, imparting practical insights. These real-world examples show the concrete effects of incorporating these technologies into cybersecurity, from spotting unusual user behavior to detecting zero-day exploits, and they end with an outlook on the application of AI and ML in threat detection in the future. It sees these technologies evolving further over time, possibly embracing new developments like federated learning and explainable artificial intelligence. The necessity of interdisciplinary cooperation and continuous research is emphasized as a means of keeping up with the ever-changing threat environment. The goal of this foresight is to guarantee that AI and ML will continue to be effective defenses against sophisticated cyberattacks. This book chapter, which is intended to serve as a complete resource, addresses the needs of academics, researchers, and professionals in the field of cyber security by offering a sophisticated understanding of the mutually beneficial link between threat detection, AI, and ML. The goal of this chapter is to provide a substantial contribution to the development of cybersecurity strategies by skillfully fusing theoretical understanding with real-world applications, especially in light of the ever-present and highly complex threats.