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Title page for ETD etd-02282007-151555

Type of Document Master's Thesis
Author Lee, Christina Mei-Fang
Author's Email Address christina.m.lee1@gmail.com
URN etd-02282007-151555
Title An evaluation of machine learning techniques in intrusion detection
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Gabor Karsai Committee Chair
Douglas Fisher Committee Member
  • multilayer perceptron
  • computer security
  • artificial intelligence
  • attacker's perspective
  • user's perspective
Date of Defense 2007-01-16
Availability unrestricted
Intrusion detection allows an organization to monitor its network for possible attacks. The ability of an intrusion detection system (IDS) to distinguish correctly between attacks and normal activity is important. The use of machine learning algorithms is an active area of study in intrusion detection. Experiments have been performed with Naive Bayes, Decision Trees, and Artificial Neural Networks (ANNs) using an intrusion detection dataset. A Naive Bayes and Decision Tree algorithm programmed in Python are used, as well as the Weka Naive Bayes, J48 Decision Tree, and Multilayer Perceptron algorithms. Several subsets of the 1999 KDD Cup dataset are used to perform these experiments. An evaluation of the results, with special attention to approaches in evaluating false positives and negatives, is discussed. A novel approach to evaluating these results is shown.
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