Rademics Research Institute

Abstract

CyberPhysical Systems (CPS) are increasingly targeted by sophisticated cyber threats, necessitating advanced Intrusion Detection Systems (IDS) capable of probabilistic risk assessment and adaptive defense mechanisms. Traditional deep learningbased IDS models, while effective in pattern recognition, lack uncertainty quantification, leading to unreliable classifications in dynamic attack environments. Bayesian Deep Learning (BDL)offers a principled approach to addressing this challenge by integrating probabilistic inference for enhanced threat detection and risk estimation. This book chapter explores the role of Bayesian Neural Networks (BNNs) in IDS, emphasizing their ability to model epistemic and aleatoric uncertainties, thereby improving detection accuracy and decision confidence. the chapter presents Bayesian Hyperparameter Optimization (BHO) as a scalable solution to optimize IDS model parameters, ensuring efficient and adaptive learning against evolving attack patterns. The integration of Bayesian techniques in both anomalybased and signaturebased IDS frameworks was examined, highlighting their potential in reducing false positives and mitigating adversarial attacks., case studies and empirical evaluations are provided to demonstrate the realworld applicability of BDLdriven IDS in securing industrial control systems, smart grids, and IoT networks. The findings underscore the importance of uncertaintyaware AIdriven cybersecurity frameworks for robust and resilient CPS protection. 

Introduction

manipulations [3,4]. Traditional security mechanisms, such as firewalls and rulebased Intrusion Detection Systems (IDS), struggle to address the complexity and evolving nature of these threats [5,6]. Deep learningbased IDS models have emerged as a promising approach for detecting sophisticated cyberattacks; however, their deterministic nature limits their ability to quantify uncertainty, leading to potential misclassifications. A more resilient approach was required to ensure robust and adaptive threat detection in CPS environments [7,8].

Bayesian Deep Learning (BDL) offers a powerful framework for enhancing IDS by incorporating probabilistic reasoning and uncertainty estimation [9,10]. Unlike conventional deep learning models, which provide fixed predictions, Bayesian Neural Networks (BNNs) generate probability distributions over possible outcomes, enabling confidenceaware decisionmaking [11-13]. This capability was particularly crucial in cybersecurity, where a lack of uncertainty quantification can result in false alarms or undetected intrusions [14,15]. By capturing both epistemic uncertainty (uncertainty in the model) and aleatoric uncertainty (uncertainty in the data), BDL allows IDS to assess threats more accurately, even in adversarial scenarios [16,17]. The ability to model uncertainty enhances IDS resilience, making it more effective in detecting zeroday attacksand novel threat patterns that have not been previously observed in training data [18,19].