Self-similarity based lightweight intrusion detection method for cloud computing

Hyukmin Kwon, Taesu Kim, Song Jin Yu, Huy Kang Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

38 Citations (Scopus)


Information security is the key success factor to provide safe cloud computing services. Despite its usefulness and cost-effectiveness, public cloud computing service is hard to accept because there are many security concerns such as data leakage, unauthorized access from outside the system and abnormal activities from inside the system. To detect these abnormal activities, intrusion detection system (IDS) require a learning process that can cause system performance degradation. However, providing high performance computing environment to the subscribers is very important, so a lightweight anomaly detection method is highly desired. In this paper, we propose a lightweight IDS with self-similarity measures to resolve these problems. Normally, a regular and periodic self-similarity can be observed in a cloud system's internal activities such as system calls and process status. On the other hand, outliers occur when an anomalous attack happens, and then the system's self-similarity cannot be maintained. So monitoring a system's self-similarity can be used to detect the system's anomalies. We developed a new measure based on cosine similarity and found the optimal time interval for estimating the self-similarity of a given system. As a result, we can detect abnormal activities using only a few resources.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - Third International Conference, ACIIDS 2011, Proceedings
Number of pages10
EditionPART 2
Publication statusPublished - 2011
Event3rd International Conference on Intelligent Information and Database Systems, ACIIDS 2011 - Daegu, Korea, Republic of
Duration: 2011 Apr 202011 Apr 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6592 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other3rd International Conference on Intelligent Information and Database Systems, ACIIDS 2011
Country/TerritoryKorea, Republic of


  • Anomaly detection
  • Cloud computing
  • Information security
  • Intrusion detection
  • Lightweight
  • Self-similarity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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