Smartphone Malware Detection Model Based on Artificial Immune System

作     者：WU Bin LU Tianliang ZHENG Kangfeng ZHANG Dongmei LIN Xing 

作者机构：InformationSecurityLaboratoryNationalDisasterRecoveryTechnologyEngineeringLaboratoryBeijingUniversityofPostsandTelecommunicationsBeijing100876P.R.China People'sPublicSecurityUniversityofChinaBeijing100038P.R.China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2014年第11卷第A01期

页      面：86-92页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 081202[工学-计算机软件与理论] 

基　　金：This work was supported in part by National Natural Science Foundation of China under Grants No.61101108 National S&T Major Program under Grants No.2011ZX03002-005-01 

主　　题：artificial immune system smartphonemalware detection negative selection clonalselection 

摘      要：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.