A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks

by   Sayak Mukherjee, et al.

This paper presents a secure reinforcement learning (RL) based control method for unknown linear time-invariant cyber-physical systems (CPSs) that are subjected to compositional attacks such as eavesdropping and covert attack. We consider the attack scenario where the attacker learns about the dynamic model during the exploration phase of the learning conducted by the designer to learn a linear quadratic regulator (LQR), and thereafter, use such information to conduct a covert attack on the dynamic system, which we refer to as doubly learning-based control and attack (DLCA) framework. We propose a dynamic camouflaging based attack-resilient reinforcement learning (ARRL) algorithm which can learn the desired optimal controller for the dynamic system, and at the same time, can inject sufficient misinformation in the estimation of system dynamics by the attacker. The algorithm is accompanied by theoretical guarantees and extensive numerical experiments on a consensus multi-agent system and on a benchmark power grid model.



There are no comments yet.


page 1


Imposing Robust Structured Control Constraint on Reinforcement Learning of Linear Quadratic Regulator

This paper discusses learning a structured feedback control to obtain su...

Reinforcement Learning of Structured Control for Linear Systems with Unknown State Matrix

This paper delves into designing stabilizing feedback control gains for ...

Learning-based attacks in Cyber-Physical Systems: Exploration, Detection, and Control Cost trade-offs

We study the problem of learning-based attacks in linear systems, where ...

Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS

Cyber attacks are increasing in volume, frequency, and complexity. In re...

Authentication of cyber-physical systems under learning-based attacks

The problem of attacking and authenticating cyber-physical systems is co...

Secure Planning Against Stealthy Attacks via Model-Free Reinforcement Learning

We consider the problem of security-aware planning in an unknown stochas...

Attack Detection for Networked Control Systems Using Event-Triggered Dynamic Watermarking

Dynamic watermarking schemes can enhance the cyber attack detection capa...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.