The Role of Machine Learning in Threat Detection System
L Shammi, Sujatha Kamepalli, Radha Ranjan, Manu Vasudevan Unni, J. A. Baskar, V. Bhoopathy · Advances in information security, privacy, and ethics book series · 2025
Conventional detection approaches frequently fall behind the ever-changing complexity and frequency of cybersecurity threats. The use of machine learning (ML) has revolutionized threat detection in many different fields, including physical security systems, fraud detection, and network security. This chapter explores the use of ML models for threat identification, prediction, and mitigation, shedding light on important techniques, problems, and practical applications. It delves further into the topic by looking at potential trends, ethical concerns, and the necessity of a comprehensive strategy to protect vital systems and data. This chapter provides real-world examples of ML's application in threat detection. It explains the process and the challenges of these strategies. More and more, ML is being used in security contexts, which raises serious concerns about data privacy, model bias, and explainability. Emerging concepts such as federated learning, explainable AI, and edge computing are covered in this chapter, along with future advancements in ML-driven threat detection.