Feature Engineering for Threat Detection
Sepideh Bazzaz Abkenar, Mostafa Haghi Kashani, Mohammad Nikravan · 2025
Organizations may combat cyber hazards with the aid of cybersecurity audits and controls. The rising interest in machine learning (ML) and deep learning (DL) in various fields has led to the development of threat detection models based on these approaches. As the number of attacks increases, it is essential to defend networks with ML-based intrusion detection systems (IDS) to ensure improved accuracy and fewer false alarms. However, these systems depend on ideal features to detect threats and keep up with changing cybersecurity obstacles. Consequently, feature detection has drawn much attention as a vital component. This chapter highlights the need to adopt feature engineering for threat detection to improve the accuracy of ML models in recognizing and preventing cyber events. It accomplishes this by studying several feature engineering strategies. This chapter explores the advantages, disadvantages, applied tools, and evaluation factors while analyzing feature engineering for threat detection and providing a taxonomy. Throughout this chapter, we discuss the major challenges such as ethical and privacy-preserving, imbalanced data, dynamic feature extraction and drift concept, real-time analysis, and automated feature engineering. Scientists need to adapt or develop these issues in feature engineering for threat detection.