TY  - JOUR
AV  - none
KW  - Ant colony optimization; Artificial intelligence; Feature extraction; Learning systems; Mercury (metal); Signal detection; Signal processing; Support vector machines
KW  -  Detecting attacks; Detection methods; Feature selection methods; Intrusion Detection Systems; Network intrusion detection; Security experts; Supervised algorithm; Supervised learning approaches
KW  -  Intrusion detection
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975887120&doi=10.1007%2f978-3-319-32213-1_27&partnerID=40&md5=31c50b4594eeada395944dbd6a4df89a
ID  - scholars7879
SP  - 305
EP  - 312
Y1  - 2016///
N1  - cited By 28; Conference of International Conference on Machine Learning and Signal Processing, MALSIP 2015 ; Conference Date: 12 June 2015 Through 14 June 2015; Conference Code:176019
N2  - Network intrusion detection gained a lot of attention from the security expert. Intrusion detection system has been designed for the purpose detecting attack and comprises of detection method that can be anomaly based or it can be signature based. These detection method, however, highly depends on the quality of the input features. Supervised learning approach for the detection method finds the relationship between the feature and its class. Therefore, irrelevant, redundant, and noisy features must be eliminated before applying supervised algorithm. This can be done by feature selection method. In this paper ant colony optimization has been applied for feature selection on KDD99 dataset. The reduced dataset is validated using support vector machine. Results show that accuracy of the SVM is significantly improved with reduced feature set. © Springer International Publishing Switzerland 2016.
A1  - Mehmod, T.
A1  - Rais, H.B.M.
TI  - Ant colony optimization and feature selection for intrusion detection
SN  - 18761100
VL  - 387
PB  - Springer Verlag
JF  - Lecture Notes in Electrical Engineering
ER  -