Secure Cloud–Edge Agent AI Compromise Detection and Autonomous Recovery Using Spy Agent Verification

Abstract

The fast growth in intelligent services in Cloud-Edge infrastructure has led to the growing reliance on Autonomous Agent AI for decisions, management, and execution of services. Yet, the constant running of the Agent AI leads to security challenges because any compromised agents might show unusual behavior patterns in terms of communications, execution, resource usage, and decision-making while performing the services. Traditional AI-based security approaches are mostly concerned with attack detection and adaptation of responses but not much in terms of safeguarding Agent AI from attack and restoring the legitimate operational knowledge of the Agent AI. The proposed research presents the development of the Secure and Self-Protective Explainable Agent AI framework that can be used to ensure security and resilience of Cloud–Edge systems. In particular, the framework uses secure entity registration and verification, behavioural learning, adaptive monitoring and Spy Agent, who monitors all processes continuously and detects compromises. As soon as the compromise is detected, the compromised Agent AI is isolated, and its legitimate knowledge base is saved. Then a knowledge migration process using transfer learning takes place, where a new Agent AI instance is created and hardened, and suspicious activity is tracked. Two algorithms are developed for the secure Cloud–Edge entity verification with adaptive monitoring and Spy Agent-based compromise detection with automatic Agent AI replacement. The Self-Protective Explainable Agent AI framework provides continuous security assessment, saving of legitimate knowledge during recovery, and maintaining operations of services with a hardened agent.