Abstract
Ensuring the security and reliability of cooperative vehicle-to-vehicle
(V2V) communications is an extremely challenging task, due to the
dynamic nature of vehicular networks as well as the delay-sensitive
wireless medium. The moving target defense (MTD) paradigm has been
proposed to overcome the challenges of conventional solutions, based on
static network services and configurations. Specifically, the MTD
approach involves the dynamic altering of the network configurations to
improve resilience to cyberattacks. Nevertheless, the current MTD
solution for cooperative networks has several limitations, such as they
are not well-suited for highly dynamic environments; they require high
synchronization modules that are resource-intensive and difficult to
implement; and finally, they rely heavily on the attack-defense models,
which may not always be accurate or comprehensive to use. In this paper,
we propose an intelligent spatiotemporal diversification MTD scheme to
defend against eavesdropping attacks in cooperative V2V networks.
Specifically, we design benign random data injection patterns to meet
the security and reliability requirements of the vehicular network. Our
methodology involves modeling the configuration of vehicular relays and
data injection patterns as a Markov decision process, followed by
applying deep reinforcement learning to determine the optimal
configuration. We then iteratively evaluate the intercept probability
and the percentage of transmitted real data for each configuration until
convergence is achieved. In order to optimize the security-real data
percentage (S-RDP), we developed a two-agent framework, namely
MTD-DQN-RSS & MTD-DQN-RSS-RDP. The first agent, MTD-DQN-RSS, tries to
minimize the intercept probability by injecting additional fake data,
which in turn reduces the overall RDP, while the second agent,
MTD-DQN-RSS-RDP, attempts to inject a sufficient amount of fake data to
achieve a target S-RDP. Finally, extensive simulation results are
conducted to demonstrate the effectiveness of our proposed solution
where they improved the system security by almost 28% and 49%,
respectively compared to the conventional relay selection approach.