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- W133096757 abstract "Due to the endless emerging of new types of network intrusion, more and more attention are paid to how can the intrusion detection system solve these new types of invasion. This study used clustering techniques to distinguish abnormal data. A clustering method is unsupervised and can group data with similar characteristic into the same cluster. A new type of intrusions always has significantly different data characteristic, hence it can be detected when it can't be assigned to any known cluster. The experimental results show that although false alarm rate is not too satisfactory, the performance in identifying new types of intrusions is better than CUBID method." @default.
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- W133096757 date "2012-10-16" @default.
- W133096757 modified "2023-09-22" @default.
- W133096757 title "A Study on Clustering-Based Method for Detecting Network Intrusions with New Types" @default.
- W133096757 doi "https://doi.org/10.1109/icecc.2012.306" @default.
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