Malicious Code Family Classification Based on Multi-feature Fusion Fractional Kalman Filter

Identifying malicious code families is a prerequisite for malicious code prevention which has become a research hotspot. Aiming at the confusion of malicious code family, a malicious code detection and classification method based on Kalman filtering is proposed. The classification accuracy after salinization reached 93.5%, experimental proof that the algorithm has high accuracy and robustness.