TY - GEN
T1 - Feature Importance Ranking for Increasing Performance of Intrusion Detection System
AU - Megantara, Achmad Akbar
AU - Ahmad, Tohari
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/15
Y1 - 2020/9/15
N2 - The performance of the Intrusion Detection System (IDS) depends on the quality of the model generated in the training process. An appropriate process positively affects not only the performance but also computational time for detecting intrusions. Reliable training data can be obtained by preprocessing the dataset, which can be feature extraction, reduction, and transformation. Generally, feature selection has become the main problem. In this research, we work on that issue by developing a new method based on Feature Importance Ranking Classification. We propose to reduce the size of the dimension by combining Feature Importance Ranking to calculate the importance of each feature and Recursive Features Elimination (RFE). The results of the experiment show that the proposed method raises the performance over the existing methods. It can be proven by evaluating some metrics: Accuracy, sensitivity, specificity, and false alarm rate.
AB - The performance of the Intrusion Detection System (IDS) depends on the quality of the model generated in the training process. An appropriate process positively affects not only the performance but also computational time for detecting intrusions. Reliable training data can be obtained by preprocessing the dataset, which can be feature extraction, reduction, and transformation. Generally, feature selection has become the main problem. In this research, we work on that issue by developing a new method based on Feature Importance Ranking Classification. We propose to reduce the size of the dimension by combining Feature Importance Ranking to calculate the importance of each feature and Recursive Features Elimination (RFE). The results of the experiment show that the proposed method raises the performance over the existing methods. It can be proven by evaluating some metrics: Accuracy, sensitivity, specificity, and false alarm rate.
KW - data mining
KW - feature importance
KW - intrusion detection system
KW - network security
KW - recursive features elimination
UR - http://www.scopus.com/inward/record.url?scp=85098985814&partnerID=8YFLogxK
U2 - 10.1109/IC2IE50715.2020.9274570
DO - 10.1109/IC2IE50715.2020.9274570
M3 - Conference contribution
AN - SCOPUS:85098985814
T3 - 2020 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020
SP - 37
EP - 42
BT - 2020 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020
A2 - Hermawan, Indra
A2 - Rasyidin, Muhammad Yusuf Bagus
A2 - Huzaifa, Malisa
A2 - Ermis Ismail, Iklima
A2 - Muharram, Asep Taufik
A2 - Mardiyono, Anggi
A2 - Marcheeta, Noorlela
A2 - Kurniawati, Dewi
A2 - Yuly, Ade Rahma
A2 - Suhanda, Ariawan Andi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020
Y2 - 15 September 2020 through 16 September 2020
ER -