Smartphone Malware Detection Model Based on Artificial Immune System

• Published in: China communications Vol. 11; no. 1; pp. 86 - 92
• Main Authors: Wu, Bin, Lu, Tianliang, Zheng, Kangfeng, Zhang, Dongmei, Lin, Xing
• Format: Journal Article
• Language: English
• Published: China Institute of Communications 2014; Information Security Laboratory, National Disaster Recovery Technology Engineering Laboratory, Beijing University of Posts and Telecommunications, Beijing 100876, P.R.China%People's Public Security University of China, Beijing 100038, P.R.China
• Subjects: artificial immune system; clonal selection; Cloning; Data mining; detection; Detectors; Encoding; Feature extraction; Immune system; Malware; negative selection; smartphone malware; 人工免疫系统; 克隆选择算法; 恶意软件; 智能手机; 检测性能; 检测技术; 检测模型; 软件分析; smartphone malware; detection; clonal selection; negative selection; artificial immune system
• ISSN: 1673-5447
• DOI: 10.1109/CC.2014.7022530

In order to solve the problem that me traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model （SP-MDM） based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation.