基于机器学习和NetFPGA的智能高速入侵防御系统

当前网络安全面临着日益多样化的威胁和挑战。入侵防御系统作为一种新兴的、能够动态监视并及时阻断异常网络传输行为的网络安全设备，成为目前主要的研究方向。目前主流的入侵防御系统主要通过人工预设的入侵规则集合对网络流进行匹配来发现、处理入侵，这种方法效率低下、维护困难，且存在严重的处理速度与成本的矛盾。针对上述问题，文章提出了结合基于硬件的网络数据流高速捕获过滤、经典机器学习技术以及当前人工智能领域前沿的深度学习自编码技术的入侵检测新思路，实现了基于NetFPGA的智能、高速的网络入侵防御系统，并在测试中取得了优于其他同一成本水平入侵检测系统的结果。

High-Speed Intelligent Internet Intrusion Defense System based on Deep Learning and NetFPGA

In this day and age, the security of Internet is faced with a growing number of threats from various sources. As a newly-emerging Internet security device which can dynamically monitor and block abnormal Internet transmission operations.Intrusion Detection System （IDS） has drawn enormous attention from the researchers. At present, the mainstream of IDS detects and handles the intrusion by matching the rules in the pre-made rules set. IDS based on such methods are low-efifcient, and are dififcult to maintain and strike a balance between speed and cost. To combat prolblems above, we propose a brand-new intrusion detection method which integrates the high-speed data stream capturing and ifltering based on hardware, classical machine learning techniques and Auto-encoder algorithm in Deep Learning, and realize a intelligent and high-speed IDS. Our syetem yield a good result which outperforms other IDS on the same cost level.