软件交互行为的可信性分析与态势预测研究

研究现代分布式软件系统中交互实体的行为可信性问题,关注运行期意图、情景、行为和行为效应之间的关系,采用先进的统计机器学习工具分析行为踪迹规律,提出了一个新的软件行为分析与态势预测方法.针对松散聚合的交互实体间可能产生新的交互事件和行为模式的问题,本文用分层Dirichlet过程和无限隐Markov模型对被监测的交互接口数据进行聚类确定未知交互事件,用含有未知事件的序列进行行为模式的半监督学习,由管理者将其添加到规则与知识库中.在确定未知事件和行为模式时,用Beam抽样方法较其他方法(如Gibbs抽样)有更高的数据抽样和推理效率.当知识库的行为模式达到一定规模时,系统便可以无监督地对交互行为进行分析和预测.本文用HMM的Viterbi算法分析当前交互事件的最佳序列,从而确定当前交互行为的善恶,对恶意行为及时报警,对非恶意行为的后续趋势进行主动预测.通过仿真实验证实了该方法在软件行为分析与预测上具有独特的优势.

Research on Trust Analysis and Trend Prediction of Software Interactive Behavior

The paper investigates the problems of behavior trust of interactive entities in modem distributed software systems (MDSS),pays close attention to the relationships among intentions,situations,behaviors and behavioral effects at running time,uses statistical machine learning tools to analyze the laws of behavior traces,and presents a novel behavior analysis and trend prediction method. We use hierarchical Dirichlet process and infinite hidden Markov model to converge monitored interface data to determine unknown events,and learn behavior patterns from event sequences including unknown events in terms of semi-supervised method. As determining unknown events and behavior patterns,Beam sampling has higher efficiency in sampling and inference compared with other method (e. g. ,Gibbs sampling). When behavior patterns reach a certain scale,MDSS can analyze and predict interactive behaviors in terms of unsupervised method. We adopt Viterbi algorithm of hidden Markov model to analyze optimal sequences of interactive e-vents, which help to determine good and evil of current behaviors. MDSS can send early warning for hostile behaviors, actively predict subsequent trends for non-hostile behaviors. Simulation experiments testify that the novel method has unique predominance in software behavior analysis and trend prediction.