云计算下手机人工免疫恶意代码检测模型  

Smartphone Malware Detection Model Based on Artificial Immune System in Cloud Computing

作　　者：武斌[1] 林幸[1] 李卫东 芦天亮[3] 张冬梅[1] 

机构地区：[1]北京邮电大学信息安全中心,北京100876 [2]国网滑县供电公司发展策划部,河南安阳456400 [3]中国人民公安大学网络安全保卫学院,北京100038

出　　处：《北京邮电大学学报》2015年第4期33-37,共5页Journal of Beijing University of Posts and Telecommunications

基　　金：国家自然科学基金项目(61101108)

摘　　要：提出一种适用于云计算环境的基于人工免疫的手机恶意代码检测模型.提出扩展阴性选择算法,提取恶意代码的特征编码生成抗原,增加针对高亲和度检测器的克隆和变异算子,提高成熟检测器的生成效率,在特征检测和检测器生成阶段引入MapReduce并行处理机制,提高计算效率.仿真结果表明,检测模型对未知手机恶意代码具有较高的检测率和计算效率.A smartphone malware detection model based artificial immune system（ AIS） on the cloud was proposed. In this model,the extended negative selection algorithm is put forward and the antigens are generated by encoding the malwarecharacteristics. With addition of cloning with higher affinity detector and hyper-mutation,the detectors are generated efficiently. The computing rate is then improved significantly by parallel computing mechanism MapReduce during the feature coding and detector generation.Experiment shows that the detection modelhas a high detection rate and computing rate for unknown smartphone malware.

关 键 词：人工免疫 阴性选择 手机恶意代码 检测 云计算 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]