Research on Network Information Security Risk Assessment Based on Artificial Intelligence

Abstract

Adversarial imitation learning has become a widely used imitation learning framework. The discriminator is often trained by taking expert demonstrations and policy trajectories as examples respectively from two categories (positive vs. negative) and the policy is then expected to produce trajectories that are indistinguishable from the expert demonstrations. But in the real world, the collected expert demonstrations are more likely to be imperfect, where only an unknown fraction of the demonstrations are optimal. Instead of treating imperfect expert demonstrations as absolutely positive or negative, we investigate unlabeled imperfect expert demonstrations as they are. A positive-unlabeled adversarial imitation learning algorithm is developed to dynamically sample expert demonstrations that can well match the trajectories from the constantly optimized agent policy. The trajectories of an initial agent policy could be closer to those non-optimal expert demonstrations, but within the framework of adversarial imitation learning, agent policy will be optimized to cheat the discriminator and produce trajectories that are similar to those optimal expert demonstrations. Theoretical analysis shows that our method learns from the imperfect demonstrations via a self-paced way. Experimental results on MuJoCo and Robo-Suite platforms demonstrate the effectiveness of our method from different aspects.

Artificial intelligence for system security assurance: A systematic literature review

“…[85], [150], [102], [126], [158] Vulnerability Classi cation [112], [131], [76], [117] Vulnerability Impact Analysis [199], [66], [99] Vulnerability Analytics [139], [9], [41], [77], [128] Risk Assessment Comprehensive Risk Assessment Framework [23], [48], [95], [175], [5], [22], [75], [62], [179], [122] Quantitative Risk Analysis [176], [65], [192], [7], [196], [195], [89], [189] Risk Classi cation and Prioritization [143], [194], [8] Automated and Dynamic Risk Analysis…”

“…By enhancing the ability to predict app behavior, this dynamic assessment method supports preventive security measures. Finally, a CNN-based framework for network information security is presented in Article [179], which can adjust to real-time data in order to deliver precise and timely risk assessments. This strategy guarantees thorough and ongoing security assessments, greatly enhancing the decision-making procedures involved in network security management.…”