A Modified Hybrid Method Based on PSO, GA, and K-Means for Network Anomaly Detection

Data anomaly detection plays a vital role in protecting network security and developing technology. Aiming at the problems of large data volume, complex information, difficult identification, this paper constructs modified hybrid (MHAD) method based on K-means clustering algorithm, particle swarm optimization, genetic algorithm. First, by designing coding rules fitness functions, multiattribute is effectively clustered, inheritance good attributes guaranteed. Second, applying selection, crossover, mutation operators to position velocity updates, local optima are avoided population diversity ensured. Finally, Fisher score expression for attribute extraction constructed, which reduces required sample size improves efficiency. The experimental results show that MHAD has better performance than support vector machine, decision trees, other methods four indicators recall, precision, prediction accuracy, F-measure. main advantages proposed it achieves balance between global search ensures high rate low false positive rate.