Predicting the resource consumption of network intrusion detection systems

SIGMETRICS'08: Proceedings of the 2008 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

Volumn 36, Issue 1 SPECIAL ISSUE, 2008, Pages 437-438

  Dreger, Holger ^a   Feldmann, Anja ^b   Paxson, Vern ^c   Sommer, Robin ^d  

^a SIEMENS AG   (Germany)

^b DEUTSCHE TELEKOM LABORATORIES   (Germany)

^c ICSI/LBNL

^d LBNL/ICSI

Author keywords

NIDS; Performance model

Indexed keywords

DETECTION ACCURACIES; MEMORY CONSUMPTIONS; NETWORK INTRUSION DETECTION SYSTEMS; NIDS; PERFORMANCE MODEL; RESOURCE CONSUMPTIONS; RESOURCE REQUIREMENTS;

COMPUTER CRIME; COMPUTER SYSTEMS; FORECASTING; INTERNET; SECURITY OF DATA; SIGNAL DETECTION;

INTRUSION DETECTION;

EID: 57349093420     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1375457.1375509     Document Type: Conference Paper

References (6)

1. H. Dreger. Operational Network Intrusion Detection: Resource-Analysis Tradeoffs. PhD thesis, TU München, 2007.
2. H. Dreger, A. Feldmann, V. Paxson, and R. Sommer. Operational Experiences with High-Volume Network Intrusion Detection. In ACM Computer and Communications Security, 2004.
3. V. Paxson. Bro: A system for detecting network intruders in real-time. Computer Networks, 31(23-24):2435-2463, 1999.
4. M. Roesch. Snort: Lightweight intrusion detection for networks. In Proc. Systems Administration Conference, 1999.
5. R. Sommer and V. Paxson. Enhancing Byte-Level Network Intrusion Detection Signatures with Context. In ACM Computer and Communications Security, 2003.
6. tcp-reduce, http://ita.ee.lbl.gov/html/contrib/tcp-reduce.html.