TY - GEN
T1 - Feature selection using genetic algorithm to improve classification in network intrusion detection system
AU - Ferriyan, Andrey
AU - Thamrin, Achmad Husni
AU - Takeda, Keiji
AU - Murai, Jun
N1 - Funding Information:
This research was supported by Indonesia Endowment Fund for Education (LPDP)
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/19
Y1 - 2017/12/19
N2 - In this paper, we present Genetic Algorithm based optimized feature selections for intrusion detection systems. We used one-point crossover for the Genetic Algorithm parameters instead of two-point crossover used by the previous research as it one-point crossover is faster. For evaluations, we used the NSL-KDD Cup 99 data set and we modified the data set by looking into to the recent attacks, hence making the data set more relevant to the current situations. Several classifiers were used on these data sets and we found that Random Forest gave the best results in terms of the classification rate and the training time. The results also showed that our parameters performed better in these two metrics and the classifications using our optimized features on the modified data sets gave mixed results compared to ones with the original features.
AB - In this paper, we present Genetic Algorithm based optimized feature selections for intrusion detection systems. We used one-point crossover for the Genetic Algorithm parameters instead of two-point crossover used by the previous research as it one-point crossover is faster. For evaluations, we used the NSL-KDD Cup 99 data set and we modified the data set by looking into to the recent attacks, hence making the data set more relevant to the current situations. Several classifiers were used on these data sets and we found that Random Forest gave the best results in terms of the classification rate and the training time. The results also showed that our parameters performed better in these two metrics and the classifications using our optimized features on the modified data sets gave mixed results compared to ones with the original features.
KW - feature selection
KW - genetic algorithm
KW - intrusion detection
KW - random forest
UR - http://www.scopus.com/inward/record.url?scp=85046542002&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046542002&partnerID=8YFLogxK
U2 - 10.1109/KCIC.2017.8228458
DO - 10.1109/KCIC.2017.8228458
M3 - Conference contribution
AN - SCOPUS:85046542002
T3 - Proceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
SP - 46
EP - 49
BT - Proceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
A2 - Bagar, Fahim Nur Cahya
A2 - Zainudin, Ahmad
A2 - Al Rasyid, M. Udin Harun
A2 - Briantoro, Hendy
A2 - Akbar, Zulhaydar Fairozal
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
Y2 - 26 September 2017 through 27 September 2017
ER -