Immune cooperation mechanism based learning framework

• Published in: Neurocomputing (Amsterdam) Vol. 148; pp. 158 - 166
• Main Authors: Zhang, Pengtao, Tan, Ying
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier B.V 19.01.2015; Elsevier
• Subjects: Applied sciences; Artificial immune system; Artificial intelligence; Biological and medical sciences; Computer science; control theory; systems; Data processing. List processing. Character string processing; Exact sciences and technology; Feature extraction; Fundamental and applied biological sciences. Psychology; General aspects; Immune cooperation mechanism; Learning and adaptive systems; Learning framework; Malware detection; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Memory and file management (including protection and security); Memory organisation. Data processing; Software; Learning framework; Feature extraction; Immune cooperation mechanism; Malware detection; Artificial immune system; Local search; Artificial life; Modeling; Biological system; Antigen; Experimental result; Biomimetics; Classification; Selection criterion; Artificial intelligence; Computer security; Pattern extraction; Immune system; Intrusion detection systems
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2012.08.076

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. •Immune cooperation based learning-ICL utilizes cooperation effect of immune signals.•ICL framework takes advantage of real-valued immune signals instead of binary ones.•The danger zone is an unnecessary step for artificial immune system (AIS).•The antigen-specific-nonspecific features are explicitly defined in this paper.•ICL-MD model greatly outperforms global/local concentration based approaches.