NDLI: Network surveillance and multi-group intrusion classification

Content Provider	IEEE Xplore Digital Library
Author	Gang Kou Nian Yan Yi Peng Yong Shi Zhengxin Chen
Copyright Year	2005
Description	Author affiliation: Peter Kiewit Inst. of Inf. Sci., Technol. & Eng., Nebraska Univ., Omaha, NE, USA (Gang Kou; Nian Yan; Yi Peng; Yong Shi; Zhengxin Chen)
Abstract	The early and reliable detection of malicious attacks is a crucial issue for today's network security and survivability. Different types of attacks may need different responses. Therefore, it is a meaningful task to predict the category of malicious attacks and take appropriate reactions. The goal of this paper is to apply multiple-criteria linear programming (MCLP) method to the multi-group intrusion classification problem. Specifically, we first collect a multi-group network intrusion dataset using Tenable NeWT Security Scanner. Five attack types and total of 9061 data records were captured. After that, MCLP five-group model was applied to the NeWT dataset. The classification accuracy of MCLP was compared with see5, a decision-tree-based classification tool. The experimental results of this research indicate that MCLP achieves comparable classification accuracy to see5.
Starting Page	844
Ending Page	848
File Size	852164
Page Count	5
File Format	PDF
ISBN	0780389719
DOI	10.1109/ICSSSM.2005.1500112
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering Information science Surveillance Data security Linear programming Data engineering Knowledge management IP networks Local area networks Classification tree analysis
Content Type	Text
Resource Type	Article

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