DESOLATER: Deep Reinforcement Learning-Based Resource Allocation and Moving Target Defense Deployment Framework

The recent development of autonomous driving technologies has led to the proliferation of research on sensors and electronic equipment inside a vehicle. To deal with security concerns of in-vehicle networks, various deep learning (DL) and reinforcement learning (RL) have been developed to enhance in-vehicle security. However, the DL/RL agents are vulnerable to adversarial perturbation, where an attacker can perform a manipulation attack to interfere with the agent’s operation. In this work, we aim to develop two key mechanisms to build secure in-vehicle networks: (1) RL-based proactive defense mechanism to achieve multiple objectives of minimizing system security vulnerabilities while maximizing service availability; and (2) a resilient RL method that allows an agent to operate in the presence of adversarial disturbances that neutralize the system security. To this end, we propose, DESOLATER (
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amework), which is a multi-agent deep reinforcement learning (mDRL)-based network slicing technique that can help determine two key network management decisions: (1) link bandwidth allocation to meet quality-of-service (QoS) requirements; and (2) the frequency of triggering IP shuffling as a proactive defense mechanism not to hinder service availability by maintaining normal system operations. We also introduce an anomaly detection mechanism with a memory-based RL technique to enhance the resiliency of the RL agents in a partially observable environment under the situation that adversarial attackers manipulating observation information. Through extensive simulation experiments, we validate that the proposed robust mDRL algorithm can help the deployed proactive security mechanism achieve both security and network performance improvement in the presence of adversarial attacks.