Implementation of Fuzzy c-Means and Outlier Detection for Intrusion Detection with KDD Cup 1999 Data Set
نویسندگان
چکیده
In this paper, a two-phase method for computer network intrusion detection is proposed. In the first phase, a set of patterns (data) are clustered by the fuzzy c-means algorithm. In the second phase, outliers are constructed by a distance-based technique and a class label is assigned to each pattern. The KDD Cup 1999 data set is used for the experiment. The results show that, for binary classification (i.e., normal or attack), the proposed method achieves a higher detection rate and a greater overall accuracy than the fuzzy c-means algorithm. Keywords— Clustering, fuzzy c-means, intrusion detection, KDD Cup 1999 data set, outlier detection
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