Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 46-49 |
| Number of pages | 4 |
| Volume | 2017-January |
| ISBN (Electronic) | 9781538607169 |
| DOIs | |
| Publication status | Published - 2017 Dec 19 |
| Event | 6th International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017 - Surabaya, Indonesia Duration: 2017 Sep 26 → 2017 Sep 27 |
Other
| Other | 6th International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017 |
|---|---|
| Country | Indonesia |
| City | Surabaya |
| Period | 17/9/26 → 17/9/27 |
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Keywords
- feature selection
- genetic algorithm
- intrusion detection
- random forest
ASJC Scopus subject areas
- Artificial Intelligence
- Signal Processing
Cite this
Feature selection using genetic algorithm to improve classification in network intrusion detection system. / Ferriyan, Andrey; Thamrin, Achmad Husni; Takeda, Keiji; Murai, Jun.
Proceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 46-49.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
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
VL - 2017-January
SP - 46
EP - 49
BT - Proceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -