摘要
随着移动应用的普及,作为恶意行为识别的基础,移动应用端的行为模式分析也成为当前研究热点。本文创新地从系统环境数据入手,通过对系统多方面数据的监控,建立隐马尔可夫模型,使用该模型对后续行为产生的系统环境数据进行隐马尔科夫估值计算,从而实现对后续行为模式的识别,同时在后续识别过程中不断优化模型。本文通过实验证明该方式具有一定有效性,为移动应用端行为模式识别提供了更多可能。
With the popularization of mobile applications, as the basis for recognition malicious behavior, behavior pattern analysis of mobile application terminal has become a hotspot of current research. This paper, starting from system environmental data, and by monitoring many aspects of system data to establish Hidden Markov Model, uses this model to take hidden Markov valuation calculation for the system environmental data generated by the subsequent behavior, so as to realize the recognition of subsequent behavior patterns.Meanwhile in the subsequent recognition process, the model has to be continuously optimized. Through experiments, it shows that the approach has some validity, in order to provide more possibilities for behavior pattern recognition of mobile application terminal.
出处
《价值工程》
2016年第19期173-175,共3页
Value Engineering
基金
云南省教育厅科学研究基金项目基于动态检测的Android平台应用程序恶意行为分析研究(编号:1405178332)
关键词
移动应用端
隐马尔可夫模型
行为模式
mobile application terminal
Hidden Markov Models
behavior pattern
