深度学习技术在网络反恐领域中的应用进展研究

Research on the Application Progress of Deep Learning in Anti-Cyber Terrorism

[目的/意义]互联网的发展在给人们生活带来便利的同时,也给网络恐怖主义的滋生创造了良好的发展空间。近年来随着人工智能领域的兴起,深度学习技术逐渐被应用到网络反恐领域的研究当中。[方法/过程]利用文献统计分析方法,分别分析深度学习技术在涉恐情报挖掘、组织结构分析和网络反恐预警三个领域的应用现状。[结果/结论]深度学习技术在自然语言处理、语音识别、计算机视觉等学科领域的发展为其在网络反恐领域的应用奠定了技术基础和支持,并为网络反恐领域的研究提供了一种更加智能高效的解决手段。但由于当前网络环境的复杂性和多变性等原因,深度学习技术仍无法完全满足网络反恐的现实需求。未来国内网络反恐研究应更加注重与前沿技术的结合,网络反恐工作可以从以下三方面展开:一是利用基于深度学习的自然语言处理、语音识别、计算机视觉等技术,对网络涉恐数据进行深度挖掘,构建我国网络反恐知识库;二是将深度学习与大数据挖掘、云计算等新兴网络技术相融合,构建我国网络反恐预警系统;三是将深度学习技术用于对恐怖组织的实时监控和打击中。

[Purpose/significance]The development of Internet not only brings convenience to people's life,but also breeds cyber terrorism.In recent years,deep learning technology has gradually been applied to anti-cyber terrorism research due to the rise of artificial intelligence.[Methods/process]Through the analysis of literature,this paper analyzes the status quo of the application of deep learning technology in the following three fields:terrorism-related information mining,organizational and structural analysis and anti-cyber terrorism early warning.[Result/conclusion]Deep learning,the penetration of which into natural language processing,speech recognition,computer vision and other disciplines,has laid the technical foundation and support for its application in anti-cyber terrorism,and has provided a more intelligent and efficient solution for the research in anti-cyber terrorism.However,as the current cyberspace environment is complex and variable,deep learning still cannot fully meet the practical needs of anti-cyber terrorism.In the future,the research of network anti-terrorism in China should pay more attention to the combination with the cutting-edge technology.The work of network anti-terrorism can be carried out from the following three aspects:first,using natural language processing,speech recognition,computer vision and other technologies based on in-depth learning to excavate in-depth the data related to network anti-terrorism and build the knowledge base of network antiterrorism in China;second,combining in-depth learning with big data mining and cloud computing;third,using deep learning technology in the real-time monitoring and attacking of terrorist organizations.