Resilient Learning-Based Control Under Denial-of-Service Attacks

TL;DR

This paper introduces a reinforcement learning-based control method that maintains stability of linear systems under denial-of-service attacks by learning optimal policies from data and establishing attack duration bounds.

Contribution

It presents a novel resilient reinforcement learning approach for linear systems under DoS attacks, with stability guarantees and practical validation.

Findings

Achieves an upper bound on DoS duration for stability

Demonstrates effectiveness on an inverted pendulum system

Provides resilience analysis under attack conditions

Abstract

In this paper, we have proposed a resilient reinforcement learning method for discrete-time linear systems with unknown parameters, under denial-of-service (DoS) attacks. The proposed method is based on policy iteration that learns the optimal controller from input-state data amidst DoS attacks. We achieve an upper bound for the DoS duration to ensure closed-loop stability. The resilience of the closed-loop system, when subjected to DoS attacks with the learned controller and an internal model, has been thoroughly examined. The effectiveness of the proposed methodology is demonstrated on an inverted pendulum on a cart.