Open Access

ARTICLE

An Intrusion Detection Algorithm Based on Feature Graph

Xiang Yu¹, Zhihong Tian², Jing Qiu^2,*, Shen Su^2,*, Xiaoran Yan³

1 School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China.
2 Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China.
3 Indiana University Network Science Institute, Bloomington, Indiana, 47408, USA.

* Corresponding Authors: Jing Qiu. Email: ;
  Shen Su. Email: .

Computers, Materials & Continua 2019, 61(1), 255-274. https://doi.org/10.32604/cmc.2019.05821

Abstract

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.

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Citations