Multi-hop Network Security Strategy Integrating ACO Algorithm and PSO Algorithm

Abstract

Multi-hop networks are widely used due to their wide coverage and strong adaptability. However, multi-hop networks are prone to attacks and user privacy breaches when transmitting data between nodes. Therefore, a multi-hop network security protection strategy binding particle swarm optimization algorithm and ant colony algorithm was proposed. Support vector machine was utilized for data sorting, and the particle swarm algorithm was improved using inertia weight coefficients. The heuristic function and pheromone update strategy of ant colony algorithm was optimized, and the penalty factor and kernel function of support vector machine were optimized using fusion algorithm. The experimental results showed that the information exposure probability of the fusion algorithm decreased from the initial 0.35% to 0.10%, the detection accuracy was 2.9% higher than that of the second-best method, respectively, and the hazardous response time, disposal time, and root-mean-square error were faster than that of the second-best method by 19.9ms, 22.7ms, and -2.3ms. The running cost of the fusion algorithm was 210 datasets lower than that of the second-best method, and the average computation time was only 27.2ms higher than the normal support vector machine, and the time complexity was lower for all of them. From this, it can be concluded that fusion algorithms can effectively enhance the detection capability of abnormal data, reduce the probability of user privacy data exposure, decrease algorithm operating costs, and improve the response and handling speed of multi-hop networks when facing attacks.