An Intrusion Detection Algorithm Based on Feature Graph

With the development of Information technology and the popularization of Internet, whenever and wherever possible, people can connect to the Internet optionally. Meanwhile, the security of network traffic is threatened by various of online malicious behaviors. The aim of an intrusion detection system (IDS) is to detect the network behaviors which are diverse and malicious. Since a conventional firewall cannot detect most of the malicious behaviors, such as malicious network traffic or computer abuse, some advanced learning methods are introduced and integrated with intrusion detection approaches in order to improve the performance of detection approaches. However, there are very few related studies focusing on both the effective detection for attacks and the representation for malicious behaviors with graph. In this paper, a novel intrusion detection approach IDBFG (Intrusion Detection Based on Feature Graph) is proposed which first filters normal connections with grid partitions, and then records the patterns of various attacks with a novel graph structure, and the behaviors in accordance with the patterns in graph are detected as intrusion behaviors. The experimental results on KDD-Cup 99 dataset show that IDBFG performs better than SVM (Supprot Vector Machines) and Decision Tree which are trained and tested in original feature space in terms of detection rates, false alarm rates and run time.     

基于LINUX的蜜网系统的实现

蜜网作为一种主动的安全防御技术被引入到网络安全领域。它的价值在于希望被攻击和侵入。以便获得黑客更多的价息和攻击技术。文中从蜜网的基本理论出发,给出了基于LINUX的蜜网系统的实现,最后对其特点进行分析并展望了未来的发展方向。     

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随着各种各样的网络攻击方法的出现,使得现在的网络变得越来越脆弱,我们如果想获得这场网络保卫战的胜利,首先应该了解这些网络攻击的特征,这样才能对症下药,及时制止这些攻击行为。本文列举了一些典型的网络攻击,将将它们进行了分类,在分析其攻击原理的基础上,针对网络攻击的具体防御措施进行了讨论和分析。     

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