TY - GEN
T1 - An investigation of classification algorithms for intrusion detection system - A quantitative approach
AU - Varghese, Josy Elsa
AU - Muniyal, Balachandra
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/30
Y1 - 2017/11/30
N2 - Nowadays security issues are growing in a tremendous rate. So it is expedient to have a mechanism to keep track of its security issues in the network or host. The Intrusion Detection System (IDS) has a critical part for supervising the networks. The false alarm rate and accuracy are the two important factors to be considered in the design of competent IDS. The role of classification algorithms is indispensable in the decision making of IDS. The redundant and irrelevant features of dataset also affects the performance of classifiers which in turn degrading the evaluation of anomaly detection. The proposed work focuses on the detailed study of different classifiers on two feature selection techniques using NSL-KDD dataset, where Random Forest on Principal Component Analysis (PCA) gives the accuracy rate of 99.52% and false alarm rate is 0.48%.
AB - Nowadays security issues are growing in a tremendous rate. So it is expedient to have a mechanism to keep track of its security issues in the network or host. The Intrusion Detection System (IDS) has a critical part for supervising the networks. The false alarm rate and accuracy are the two important factors to be considered in the design of competent IDS. The role of classification algorithms is indispensable in the decision making of IDS. The redundant and irrelevant features of dataset also affects the performance of classifiers which in turn degrading the evaluation of anomaly detection. The proposed work focuses on the detailed study of different classifiers on two feature selection techniques using NSL-KDD dataset, where Random Forest on Principal Component Analysis (PCA) gives the accuracy rate of 99.52% and false alarm rate is 0.48%.
UR - https://www.scopus.com/pages/publications/85042802384
UR - https://www.scopus.com/inward/citedby.url?scp=85042802384&partnerID=8YFLogxK
U2 - 10.1109/ICACCI.2017.8126146
DO - 10.1109/ICACCI.2017.8126146
M3 - Conference contribution
AN - SCOPUS:85042802384
VL - 2017-January
T3 - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
SP - 2045
EP - 2051
BT - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Y2 - 13 September 2017 through 16 September 2017
ER -