Route Mutation Research Articles

Toward Attack-Resistant Route Mutation for VANETs: An Online and Adaptive Multiagent Reinforcement Learning Approach

Vehicular Ad hoc Networks (VANETs) are prone to packet drop attacks because of their inherent distributed architecture and dynamic topology. Existing security schemes mainly focus on multi-path and trust-based routing. Unfortunately, the former causes high energy consumption and the latter requires trust assessment, which is not easy to implement in practice. Route mutation (RM) is emerging as an active defense technology that changes routes periodically. Traditional RM is conceived for fixed network topologies, and needs a centralized controller, so that it cannot be applied to VANETs. Therefore, the present contribution investigates RM in VANETs by proposing a Grid-based extended Joint Action Learning approach (Grid-eJAL). To the best of our knowledge, this is the first contribution that designs an online and adaptive multi-agent reinforcement learning (MARL) for RM to mitigate attacks in VANETs. Differently from existing MARL schemes, Grid-eJAL allows vehicles to share parameters to accelerate the convergence speed of learning. In Grid-eJAL, the area of interest is split in equally sized grids and, when a vehicle transmits packets, the next hop with the minimum angle of mobility is selected within the grid considered as optimal by the learning policy. The convergence of Grid-eJAL is proved theoretically. Finally, extensive simulation results highlight the effectiveness of Grid-eJAL compared to representative state-of-the-art solutions.

Context-Aware Adaptive Route Mutation Scheme: A Reinforcement Learning Approach

Moving target defense (MTD) is an emerging proactive defense technology, which can reduce the risk of vulnerabilities exploited by attacker. As a crucial component of MTD, route mutation (RM) faces a few fundamental problems defending against sophisticated Distributed-Denial of Service (DDoS) attacks: 1) it is unable to make optimal mutation selection due to insufficient learning in attack behaviors and 2) because network situation is time varying, RM also lacks self-adaptation in mutation parameters. In this article, we propose a context-aware Q-learning algorithm for RM (CQ-RM) that can learn attack strategies to optimize the selection of mutated routes. We first integrate four representative attack strategies into a unified mathematical model and formalize multiple network constraints. Then, taking above network constraints into considerations, we model RM process as a Markov decision process (MDP). To look for the optimal policy of MDP, we develop a context estimation mechanism and further propose the CQ-RM scheme, which can adjust learning rate and mutation period adaptively. Correspondingly, the optimal convergence of CQ-RM is proved theoretically. Finally, extensive experimental results highlight the effectiveness of our method compared to representative solutions.

Improved ant colony optimization algorithm for the traveling salesman problems

Ant colony optimization (ACO) is a new heuristic algorithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem (TSP) is among the most important combinatorial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature convergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups: scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route's mutation probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.