Immune Concentration-Based Malware Detection Approaches

This chapter applies the immune concentration-based malware detection approaches. The local concentration-based malware detection method connects a certain number of two element local concentration vectors as the feature vector. To achieve better detection performance, particle swarm optimization (PSO) is used to optimize the parameters of local concentration. Then the hybrid concentration-based feature extraction (HCFE) approach is presented by extracting the hybrid concentration (HC) of malware in both the global resolution and the local resolution. Self detector library are composed of detectors with maximum representative of benign files and nonself detector library are composed of those detectors with maximum representative of malware. Several robust optimization approaches can be employed to optimize the input vector, like PSO and genetic algorithm (GA). The hybrid concentration-based feature extraction approach extracts the hybrid concentration of a sample in both the global resolution and the local resolution.