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Intrusion detection approaches are commonly divided into two categories: misuse detection and anomaly detection

A Fuzzy Data Mining Based Intrusion Detection Model

FTDCS, pp.191-197, (2004)

Cited: 57|Views36
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Abstract

Mass user adoption is often a good indication of atechnology's success. We argue that the vision laid out bysome distributed computing system (DCS) pioneer that"almost everyone who uses a pencil will use a computer"is still unrealized. We review the ...

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Introduction
  • The ubiquitous use of computers and computer networks in today’s society has made computer network security an international priority.
  • Misuse detection systems are vulnerable to intruders who use new patterns of behavior or who mask their illegal behavior to deceive the detection system.
  • With the anomaly detection approach, one represents patterns of normal behavior, with the assumption that an intrusion can be identified based on some deviation from this normal behavior.
  • When such a deviation is observed, an intrusion alarm is produced.
Highlights
  • The ubiquitous use of computers and computer networks in today’s society has made computer network security an international priority
  • Intrusion detection approaches are commonly divided into two categories: misuse detection and anomaly detection [1]
  • Misuse detection systems are vulnerable to intruders who use new patterns of behavior or who mask their illegal behavior to deceive the detection system
  • With the anomaly detection approach, one represents patterns of normal behavior, with the assumption that an intrusion can be identified based on some deviation from this normal behavior
  • This paper describes a prototype intelligent intrusion detection system (IIDS) that is being developed to demonstrate the effectiveness of data mining techniques that utilize fuzzy logic
  • We have found that genetic algorithms can be successfully used to tune the membership functions of the fuzzy sets used by our intrusion detection system [13]
Conclusion
  • The authors have integrated data mining techniques with fuzzy logic to provide new techniques for intrusion detection.
  • The authors' system architecture allows them to support both anomaly detection and misuse detection components at both the individual workstation level and at the network level.
  • Both fuzzy and non-fuzzy rules are supported within the system.
  • The authors plan to extend this system to operate in a high performance cluster computing environment
Reference
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