Immune cooperation mechanism based learning framework

Inspired from the immune cooperation (IC) mechanism in biological immune system (BIS), this paper proposes an IC mechanism based learning (ICL) framework. In this framework, a sample is expressed as an antigen-specific feature vector and an antigen-nonspecific feature vector at first, respectively, simulating the antigenic determinant and danger features in the BIS. The antigen-specific and antigen-nonspecific classifiers score the two vectors and export real-valued Signal 1 and Signal 2, respectively. With the cooperation of the two signals, the sample is classified by the cooperation classifier, which resolves the signal conflict problem at the same time. The ICL framework simulates the BIS in the view of immune signals and takes full advantage of the cooperation effect of the immune signals, which improves the performance of the ICL framework. It does not involve the concept of the danger zone and further suggests that the danger zone is considered to be unnecessary in an artificial immune system (AIS). Comprehensive experimental results demonstrate that the ICL framework is an effective learning framework. The ICL framework based malware detection model outperforms the global concentration based malware detection approach and the local concentration based malware detection approach for about 3.28% and 2.24% with twice faster speed, respectively.