Sampling distance analysis of gigantic data mining for intrusion detection systems

Authors:
Yong Zeng;Jianfeng Ma
Affiliations:
Key Laboratory of Computer Networks and Information Security, Ministry of EducationXidian University, Xi’an, China;Key Laboratory of Computer Networks and Information Security, Ministry of EducationXidian University, Xi’an, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Year:
2005

Citing 2
Cited 0

Statistical Traffic Modeling for Network Intrusion Detection

MASCOTS '00 Proceedings of the 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Fundamental design issues for the future Internet

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Real-Time intrusion detection system (IDS) based on traffic analysis is one of the highlighted topics of network security researches. Restricted by computer resources, real-time IDS is computationally infeasible to deal with gigantic operations of data storage and analyzing in real world. As a result, the sampling measurement technique in a high-speed network becomes an important issue in this topic. Sampling distance analysis of gigantic data mining for IDS is shown in this paper. Based on differential equation theory, a quantitative analysis of the effect of IDS on the network traffic is given firstly. Secondly, a minimum delay time of IDS needed to detect some kinds of intrusions is analyzed. Finally, an upper bound of the sampling distance is discussed. Proofs are given to show the efficiency of our approach.