Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions

TL;DR

This survey addresses the challenge of securing Industry 5.0 environments by reviewing Explainable AI-based Intrusion Detection Systems (X-IDS). It presents a structured taxonomy of explainability (ante-hoc vs post-hoc; self-model, pre-modeling, and post-model), overviews existing X-IDS approaches, and discusses adversarial XAI threats and defenses. The paper also surveys datasets, explainability techniques (e.g., LIME, SHAP), and the dual-use nature of explanations that can both bolster and compromise security. Major findings highlight persistent challenges in complexity, scalability, and standardization, as well as the need for adversarially robust, context-aware explanations to support high-stakes Industry 5.0 applications. The work offers directions for future research toward trustworthy, interpretable, and resilient IDS solutions in cyber-physical industrial systems.

Abstract

Industry 5.0, which focuses on human and Artificial Intelligence (AI) collaboration for performing different tasks in manufacturing, involves a higher number of robots, Internet of Things (IoTs) devices and interconnections, Augmented/Virtual Reality (AR), and other smart devices. The huge involvement of these devices and interconnection in various critical areas, such as economy, health, education and defense systems, poses several types of potential security flaws. AI itself has been proven a very effective and powerful tool in different areas of cybersecurity, such as intrusion detection, malware detection, and phishing detection, among others. Just as in many application areas, cybersecurity professionals were reluctant to accept black-box ML solutions for cybersecurity applications. This reluctance pushed forward the adoption of eXplainable Artificial Intelligence (XAI) as a tool that helps explain how decisions are made in ML-based systems. In this survey, we present a comprehensive study of different XAI-based intrusion detection systems for industry 5.0, and we also examine the impact of explainability and interpretability on Cybersecurity practices through the lens of Adversarial XIDS (Adv-XIDS) approaches. Furthermore, we analyze the possible opportunities and challenges in XAI cybersecurity systems for industry 5.0 that elicit future research toward XAI-based solutions to be adopted by high-stakes industry 5.0 applications. We believe this rigorous analysis will establish a foundational framework for subsequent research endeavors within the specified domain.

Figures (15)

• Figure 1: Evolution of Industry 5.0: A visual representation highlighting the trajectory of smart industry development underscored by the imperative for robust cybersecurity measures in the face of escalating cyber attacks.
• Figure 2: XAI Taxonomies
• Figure 3: The evolution of conventional Intrusion Detection Systems (IDSs) to the Moderen IDSs.
• Figure 4: Explainable AI-Based Intrusion Detection Systems
• Figure 5: Explainable AI-Based Intrusion Detection Systems - Continued