• Title/Summary/Keyword: online incremental mining

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An Online Response System for Anomaly Traffic by Incremental Mining with Genetic Optimization

  • Su, Ming-Yang;Yeh, Sheng-Cheng
    • Journal of Communications and Networks
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    • v.12 no.4
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    • pp.375-381
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    • 2010
  • A flooding attack, such as DoS or Worm, can be easily created or even downloaded from the Internet, thus, it is one of the main threats to servers on the Internet. This paper presents an online real-time network response system, which can determine whether a LAN is suffering from a flooding attack within a very short time unit. The detection engine of the system is based on the incremental mining of fuzzy association rules from network packets, in which membership functions of fuzzy variables are optimized by a genetic algorithm. The incremental mining approach makes the system suitable for detecting, and thus, responding to an attack in real-time. This system is evaluated by 47 flooding attacks, only one of which is missed, with no false positives occurring. The proposed online system belongs to anomaly detection, not misuse detection. Moreover, a mechanism for dynamic firewall updating is embedded in the proposed system for the function of eliminating suspicious connections when necessary.

Data Streams classification using Local Concept-adapted IOLIN System (지역적 컨셉트 적응형 IOLIN시스템을 사용한 데이터 스트림의 분류)

  • Kim, Jae-Woo;Song, Jae-Won;Lee, Ju-Hong
    • Journal of the Korea Society of Computer and Information
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    • v.13 no.1
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    • pp.37-44
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    • 2008
  • Data stream has the tendency to change in Patterns over time. Also known as concept drift, such problem can reduce the predictive performance of a classification model CVFDT and IOLIN tried to solve the problem of a concept drift through incremental classification model updates. The local changes in patterns. however was revealed to be unable to resolve the problems of local concept drift that occurs by influencing on total classification results. In this paper, we propose adapted IOLIN system that improves system's predictive performance by detecting the local concept drift. The experimental result shows that adaptive IOLIN, the Proposed method, is about 2.8% in accuracy better than IOLIN and about 11.2% in accuracy better than CVFDT.

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