Analysis of Malware Detection and Signature Generation Using a Novel Hybrid Approach

Abstract

In recent years, malware detection has become necessary to improve system performance and prevent programs from infecting your computer. Signature-based malware failed to detect most new organisms. This article presents the hybrid technique to automatically generate and classify malicious signatures. The hybrid method is called the ANFIS-SSA approach. The hybrid system includes the Adaptive Neuro Fuzzy Interference System (ANFIS) and the Salp Swarm Optimization (SSA). Based on this observation, we propose a hybrid approach to detect malware using malware terminology and its API calls to each other. We create the master signature for the entire malware category, not the malicious template. This signature can also identify unknown extended variants of this class. We show our approach in some common malware classes, which show that each extended version of the malware class is recognized by its original signature. The proposed method is integrated into the Matlab/Simulink operating system and is comparable to existing secure methods. SAFE creates an abstract model for the malicious code and converts it to an internal representation.

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