Projects per year
In this work, a new method for classification is proposed consisting of a combination of feature selection, normalization, fuzzy C means clustering algorithm and C4.5 decision tree algorithm. The aim of this method is to improve the performance of the classifier by using selected features. The fuzzy C means clustering method is used to partition the training instances into clusters. On each cluster, we build a decision tree using C4.5 algorithm. Experiments on the KDD CUP 99 data set shows that our proposed method in detecting intrusion achieves better performance while reducing the relevant features by more than 80%.
|Title of host publication||Cyberspace Safety and Security|
|Subtitle of host publication||Proceedings 5th International Symposium, CSS 2013, Zhangjiajie, China, November 13-15, 2013|
|ISBN (Print)||978-3-319-03583-3, 3319035835|
|Publication status||Published - 11 Nov 2013|
|Name||Lecture Notes in Computer Science / Security and Cryptology|
- Intrusion detection
- Fuzzy C-Means
- Feature selection
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- 1 Finished
Self-heating architectures against malware propagation
Scully, P. M. D.
01 Oct 2011 → 30 Sept 2014
Project: Externally funded research