Dynamic Cyber Deception Strategies for IIoT Security Enhancement

The Industrial Internet of Things (IIoT) faces increasing cyber threats exploiting resource shortages and vulnerabilities. Two new frameworks, Co-ADAM and D3O-IIoT, utilize reinforcement learning to enhance deception techniques against attackers. Co-ADAM achieves an Attack Mitigation Rate (AMR) of 83.63% against adaptive attackers, while D3O-IIoT reports a 13.7% AMR with minimal false alarms. Both frameworks employ Markov Decision Processes to optimize defense strategies dynamically. The studies highlight the importance of modeling adversarial skepticism and real-time threat monitoring in IIoT environments. The research emphasizes the need for adaptive defenses rather than static intrusion detection systems. Both frameworks were validated using multiple datasets, confirming their effectiveness in real-world scenarios.

Key Points: • Co-ADAM achieves an 83.63% attack mitigation rate against adaptive attackers. • D3O-IIoT reports a 13.7% attack mitigation rate with only a 0.3% false alarm rate. • Both models utilize reinforcement learning to dynamically adapt deception techniques.