Using feature selection for intrusion detection system
Alazab, Ammar, Hobbs, Michael, Abawajy, Jemal and Alazab, Moutaz 2012, Using feature selection for intrusion detection system, in ISCIT 2012 : Proceedings of the 12th IEEE International Symposium on Communications and Information Technologies, IEEE, [Piscataway, N. J.].
A good intrusion system gives an accurate and efficient classification results. This ability is an essential functionality to build an intrusion detection system. In this paper, we focused on using various training functions with feature selection to achieve high accurate results. The data we used in our experiments are NSL-KDD. However, the training and testing time to build the model is very high. To address this, we proposed feature selection based on information gain, which can detect several attack types with high accurate result and low false rate. Moreover, we executed experiments to category each of the five classes (probe, denial of service (DoS), user to super-user (U2R), and remote to local (R2L), normal). Our proposed outperform other state-of-art methods.
Field of Research
080303 Computer System Security
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category
E1 Full written paper - refereed
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