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RAAS: a reliable analyzer and archiver for snort intrusion detection system

Published: 11 March 2007 Publication History

Abstract

One of the primary challenges in IDS alerts analysis is controlling and archiving the huge amount of alerts that have been triggered mainly in attack periods. We have developed a self-adaptive controlling mechanism which archives the Snort generated alerts in a well-formed abstracted format. An appropriate hashing technique along with a full-automated time-based hierarchical archiving approach has been used to reach this end. The developed system prevents the Snort database size to grow uncontrollably and unexpectedly. Results obtained from experiments and test cases show that especially in critical attack situations the system responds to queries well in a reasonable amount of time. The developed analyzer with new archiving approach is also able to compress the generated alerts effectively and generate statistical reports fast. The developed system is platform independent and can be deployed on mid-range servers and workstations. Also employing it does not require much degree of security expertise.

References

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cover image ACM Conferences
SAC '07: Proceedings of the 2007 ACM symposium on Applied computing
March 2007
1688 pages
ISBN:1595934804
DOI:10.1145/1244002
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 March 2007

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Author Tags

  1. IDS analyzer
  2. archiving
  3. intrusion detection system
  4. snort

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Cited By

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  • (2017)Using Sports Plays to Configure Honeypots Environments to form a Virtual Security ShieldComputer and Network Security Essentials10.1007/978-3-319-58424-9_11(189-204)Online publication date: 13-Aug-2017
  • (2013)Enhancing False Alarm Reduction Using Voted Ensemble Selection in Intrusion DetectionInternational Journal of Computational Intelligence Systems10.1080/18756891.2013.8021146:4(626-638)Online publication date: Aug-2013
  • (2012)A case study: Intelligent false alarm reduction using fuzzy if-then rules in network intrusion detection2012 9th International Conference on Fuzzy Systems and Knowledge Discovery10.1109/FSKD.2012.6233768(505-509)Online publication date: May-2012
  • (2011)Visual Mining Intrusion Behaviors by Using Swarm Technology2011 44th Hawaii International Conference on System Sciences10.1109/HICSS.2011.486(1-7)Online publication date: Jan-2011
  • (2010)Multistage attack detection system for network administrators using data miningProceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research10.1145/1852666.1852722(1-4)Online publication date: 21-Apr-2010
  • (2010)Alerts visualization and clustering in network-based intrusion detectionProceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research10.1145/1852666.1852712(1-4)Online publication date: 21-Apr-2010
  • (2010)Optimal Allocation of the Active Filters Based on the Tabu Algorithm in Distribution Network2010 International Conference on Electrical and Control Engineering10.1109/iCECE.2010.351(1418-1421)Online publication date: Jun-2010
  • (2010)Alerts Analysis and Visualization in Network-based Intrusion Detection Systems2010 IEEE Second International Conference on Social Computing10.1109/SocialCom.2010.120(785-790)Online publication date: Aug-2010
  • (2010)Swarm-Based Knowledge Discovery for Intrusion Behavior Discovering2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery10.1109/CyberC.2010.56(270-275)Online publication date: Oct-2010
  • (2010)Automatic Signature Generation for Network Services through Selective Extraction of Anomalous ContentsProceedings of the 2010 Sixth Advanced International Conference on Telecommunications10.1109/AICT.2010.22(370-375)Online publication date: 9-May-2010
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