Artificial Immune Systems for Industrial Intrusion Detection: A Systematic Review and Conceptual Framework

Abstract

The rapid integration of cyberphysical systems (CPSs) and the Industrial Internet of Things (IIoT) has transformed industrial environments, driving advancements in autonomy, flexibility, and interconnectedness. However, this evolution introduces significant cybersecurity challenges, including vulnerability to sophisticated cyberattacks targeting critical infrastructure. Artificial immune systems (AISs), inspired by the human immune system′s adaptive and self-regulating mechanisms, provide a promising approach for intrusion detection in dynamic industrial networks. This study conducted a systematic literature review (SLR) following the PRISMA methodology to investigate the state-of-the-art applications, challenges, and research trends of AIS in industrial intrusion detection systems (IDSs). From an initial pool of 1320 records retrieved from databases like IEEE Xplore, Scopus, and Web of Science, 50 peer-reviewed studies were selected for in-depth analysis. The results highlight a growing adoption of AIS algorithms, including the negative selection algorithm (NSA), clonal selection algorithm (CSA), and dendritic cell algorithm (DCA), for anomaly detection, fault tolerance, and distributed control in industrial settings. Despite promising simulation-based outcomes, challenges such as real-time scalability, model interpretability, and infrastructure compatibility remain significant barriers. To address these gaps, this study proposes a novel conceptual framework that integrates AIS with multiagent systems (MASs) and blockchain technology, enabling autonomous detection, context-aware decision-making, and secure, decentralized data exchange. Future research should prioritize enhancing AIS scalability, reducing algorithmic complexity, and exploring integration with emerging technologies like explainable AI and blockchain to ensure robust, adaptive, and secure industrial systems. These findings offer critical insights for researchers and practitioners aiming to develop intelligent and resilient industrial cybersecurity solutions.