Correlation-based feature selection for intrusion detection design Conference

Chou, TS, Yen, KK, Luo, J et al. (2007). Correlation-based feature selection for intrusion detection design . 2018 IEEE MILITARY COMMUNICATIONS CONFERENCE (MILCOM 2018), 10.1109/MILCOM.2007.4454806

cited authors

  • Chou, TS; Yen, KK; Luo, J; Pissinou, N; Makki, K

authors

abstract

  • In a large amount of monitoring network traffic data, not every feature of the data is relevant to the intrusion detection task. In this paper, we aim to reduce the dimensionality of the original feature space by removing irrelevant and redundant features. A correlation-based feature selection algorithm is proposed for selecting a subset of most informative features. Six data sets retrieved from UCI databases and an intrusion detection benchmark data set, DARPA KDD99, are used to train and to test C4.5 and naive bayes machine learning algorithms. We compare our proposed approach with two correlation-based feature selection algorithms, CFS and FCBF and the results indicate that our approach achieves the highest averaged accuracies. Our feature selection algorithm could effectively reduce the size of data set. © 2007 IEEE.

publication date

  • January 1, 2007

published in

Digital Object Identifier (DOI)