A Survey of the High-Speed Self-learning Intrusion Detection Research Area

Authors:
Anna Sperotto;Remco Meent
Affiliations:
University of Twente, The Netherlands;University of Twente, The Netherlands
Venue:
AIMS '07 Proceedings of the 1st international conference on Autonomous Infrastructure, Management and Security: Inter-Domain Management
Year:
2007

Citing 7
Cited 0

Operational experiences with high-volume network intrusion detection

Proceedings of the 11th ACM conference on Computer and communications security
Intrusion Detection and Correlation: Challenges and Solutions

Intrusion Detection and Correlation: Challenges and Solutions
Host Behaviour Based Early Detection of Worm Outbreaks in Internet Backbones

WETICE '05 Proceedings of the 14th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprise
A Framework for Real-Time Worm Attack Detection and Backbone Monitoring

IWCIP '05 Proceedings of the First IEEE International Workshop on Critical Infrastructure Protection
Efficient Packet Matching for Gigabit Network Intrusion Detection using TCAMs

AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 01
A DoS Resilient Flow-level Intrusion Detection Approach for High-speed Networks

ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Detection of Super Sources and Destinations in High-Speed Networks: Algorithms, Analysis and Evaluation

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Intrusion detection for IP networks has been a research theme for a number of years already. One of the challenges is to keep up with the ever increasing Internet usage and network link speeds, as more and more data has to be scanned for intrusions. Another challenge is that it is hardly feasible to adapt the scanning configuration to new threats manually in a timely fashion, because of the possible rapid spread of new threats. This paper is the result of the first three months of a PhD research project in high speed, self-learning network intrusion detection systems. Here, we give an overview of the state of the art in this field, highlighting at the same time the major open issues.