الگوریتم بهینه سازی بر اساس کلونی مورچه ای بهبود یافته مبنی بر اتوماسیون سلولی برای مقابله با حملات DDoS در VANETs Cellular Automata-based Improved Ant Colony-based Optimization Algorithm for mitigating DDoS attacks in VANETs

1. Introduction In VANET, the reliability of vehicular nodes quantifies their co-operation that ensures thedegree the participation with which they forward packets for the sake of their neighbours due to the devoid of a centralized control point for communication[1]. But the extent of collaboration rendered by the vehicular nodes for forwarding packets significant decreases in the presence of malicious activity like DDoS attacks as they intentionally or unintentionally use the resources and degrade the performance of the network for the reason to remainactive in the network without a genuine cause[2-4].The presence of DDoS attacks potentially reduces the rate of packet delivery and throughput inspite of incurring a greater level of delay, control overhead and total overhead. From the past decade, a significant number of mitigation algorithms were proposed for handling DDOs attacks through the integration of meta-heuristic stochastic optimization algorithms that includes Particle swarm optimization(CA-PSO), Ant Colony Optimization(CA-ACO),Genetic algorithm and Artificial Bee Colony Algorithms(ABCA). These meta-heuristic stochastic optimization algorithms are mainly proposed for improving the global search ability for identifying and assessing quality solutions at an optimal rate [5-7]. Further meta-heuristic stochastic optimization algorithms are utilized for the following reasons[8-10] viz., i)It is suitable for resolving any issues that could be derived in a finite dimensional space for identifying an optimal solution,ii) They are experimentally proved and confirmed to be highly suitable for approximation of solution than the heuristic stochastic optimization algorithms in most real time complex environments, iii) They possess a maximum search potential that makes it highly suitable for its applicability in VANET and iv) They are proved to exhibit an higher level of precison when enhanced and integrated for maximizing the exploration extent. In this paper, Cellular Automata-based Improved Ant Colony-based Optimization Algorithm (CA-IACOA) is proposed for eliminating the concept of stagnation that exists in the traditional Ant Colony-based OptimizationAlgorithm(CA-ACO). CA-IACOA used for mitigation assures an effective and efficient global search space for identifying and replacing the DDoS compromised node with optimally elected vehicular node. The traditional CA-ACO algorithm for DDoS mitigation is improved in the following dimensions viz., i) the movements of ants are modified based on dynamic movement probability rule, ii) the pheromone updating rules are improved based on pheromone intensity constant, iii) a pheromone adaptive adjustment strategy is incorporated for modifying the non-uniform distribution of pheromone to unform distribution of pheromone and iv) Dynamic evaporation factor strategy is used for increasing the search potential that in turn enhances the rate of convergence to a considerable level.