机器学习在网络入侵检测中的应用

随着网络的快速发展,网络安全成为计算机网络中一个重要的研究方向。网络攻击日益频繁,传统的安全防护产品存在漏洞, 入侵检测作为信息安全的重要防护手段弥补了防火墙的不足,提供了有效的网络入侵检测措施,保护网络安全。然而传统的入侵检测系统存在许多问题,基于机器学习的入侵检测方法实现了对网络攻击的智能检测,提高了入侵检测的效率,降低了漏报率和误报率。本文首先简要介绍机器学习的部分算法,然后对机器学习算法在网络入侵检测中的应用进行深入的分析,比较各个算法在入侵检测应用中的优势和缺点,最后总结了机器学习的应用前景,为获得性能良好的网络入侵检测和防御系统奠定基础。

Application of Machine Learning in Network Intrusion Detection

With the development of network, network security becomes the key course of computer research. Hacker attacks become more and more frequent. The traditional security products have loopholes. Intrusion detection, as an important means of information security, makes up for the shortcomings of the firewall, provides an effective network intrusion detection measures and protects the network security. However, there are a lot of problems in traditional network intrusion detection. Methods based on machine can detect network intrusion automatically, improve the efficiency of intrusion detection, and reduce the false negative rate and false alarm rate. Here, we first introduce some machine learning algorithms briefly, and then analyze the application of machine learning algorithm in network intrusion detection. Moreover ,we compare the advantages and disadvantages of each algorithm applied in intrusion detection. Finally we summarize the application prospect of machine learning to lay the foundation for the network intrusion detection and prevention system with good performance.