Machine Learning and Deep Learning Techniques for Cybersecurity Risk Prediction and Anomaly Detection

Intrusion detection systems (IDS) play a crucial role in safeguarding modern network environments
from a wide array of cyber threats. As networks evolve, traditional detection methods based on
signature matching or rule-based techniques have proven inadequate in identifying novel and
sophisticated attacks. This chapter explores the application of autoencoders for unsupervised anomaly
detection, focusing on their effectiveness in high-dimensional security data. The chapter delves into
threshold selection strategies, which are vital in distinguishing between normal and anomalous
network behavior. A particular emphasis is placed on the challenges of balancing false positives and
false negatives, which significantly impact detection performance. Additionally, the chapter examines
dynamic thresholding techniques, including incremental learning and adaptive calibration, which
enable real-time detection and response to evolving network conditions. The integration of multifeature
data and multi-dimensional thresholding strategies is explored, demonstrating how
autoencoders can capture complex patterns and enhance detection accuracy in diverse cybersecurity
scenarios. Finally, the chapter provides a comprehensive analysis of the practical implementation of
these techniques in real-world IDS, highlighting their potential to significantly improve intrusion
detection and reduce detection latency. This work contributes to advancing the field of IDS, offering
a robust framework for designing adaptive, scalable, and efficient security systems.
Keywords: Intrusion Detection Systems, Autoencoders, Anomaly Detection, Threshold Selection,
Dynamic Thresholding, Incremental Learning.