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