基于贝叶斯网络的公安网络执法手段研究

Research on law enforcement means of public security network based on bayesian network

公安传统网络执法手段难以应对日益复杂的网络,急需探索高效灵活的新途径。贝叶斯网络作为机器学习中重要的算法,被广泛应用于人工智能领域。然而,公安网络执法中尚未有相应的应用。论文提出一种新型的贝叶斯网络进行文本感情分析的方法,实现高效率的自动化网络舆情监控,并由此实现了基于犯罪目标求解一条最优的"侦查、渗透、取证"网络执法流程,完成自动化网络犯罪打击。此外,动态贝叶斯网络能够不断根据前驱训练结果和外部因素进行反馈调整,相比静态传统手段在精度和灵活性上都更加契合公安工作的需求。

The traditional law enforcement means of public security is difficult to deal with the increasinglycomplicated network,so it is urgent to explore a new way of efficient and flexible.As an importantalgorithm in machine learning,Bayesian network is widely used in the field of artificial intelligence.However,there is no corresponding application in police network law enforcement.In this paper,a new Bayesian network for text affective analysis is proposed,which realizes an efficient automatic network monitoring of public opinion,and thus realizes an optimal"investigation,infiltration,forensics"Network law enforcement process based on the crime target to complete the automatic cybercrime attack.In addition,the dynamic Bayesian network can continuously adjust the feedback according to the result of the precursor training and external factors,which is more consistent with the demand of the public security work than the static traditional method in accuracy and flexibility.