TY - GEN
T1 - Ensemble Methods Classifier Comparison for Anomaly Based Intrusion Detection System on CIDDS-002 Dataset
AU - Ainurrochman,
AU - Nugroho, Arianto
AU - Wahyuwidayat, Raditia
AU - Sianturi, Santi Tiodora
AU - Fauzi, Muhamad
AU - Ramadhan, M. Febrianto
AU - Pratomo, Baskoro Adi
AU - Shiddiqi, Ary Mazharuddin
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - With the rapid development of information technology, the network has been everywhere. This technology has brought a lot of convenience to people, but there are also some security problems. To solve these problems, many methods have been proposed, among which is intrusion detection. A lot of research has been done to find the most effective Intrusion Detection Systems. In term of detecting novel attacks, Anomaly-Based Intrusion Detection Systems has better significance than Misuse-Based Intrusion Detection Systems. The research on the datasets being used for training and testing purposes in the detection model is as important as the model. Better dataset quality can improve intrusion detection model results. This research presents the statistical analysis of labeled flow-based CIDDS-002 dataset using ensemble methods classifier. The analysis is done concerning some prominent evaluation metrics used for evaluating Intrusion Detection Systems including Detection Rate, Accuracy, and False Positive Rate. As a result, the accuracy of the Bagging (Decision Tree) is 99.71% and Bagging (Gaussian Naïve Bayes) is 67.57%.
AB - With the rapid development of information technology, the network has been everywhere. This technology has brought a lot of convenience to people, but there are also some security problems. To solve these problems, many methods have been proposed, among which is intrusion detection. A lot of research has been done to find the most effective Intrusion Detection Systems. In term of detecting novel attacks, Anomaly-Based Intrusion Detection Systems has better significance than Misuse-Based Intrusion Detection Systems. The research on the datasets being used for training and testing purposes in the detection model is as important as the model. Better dataset quality can improve intrusion detection model results. This research presents the statistical analysis of labeled flow-based CIDDS-002 dataset using ensemble methods classifier. The analysis is done concerning some prominent evaluation metrics used for evaluating Intrusion Detection Systems including Detection Rate, Accuracy, and False Positive Rate. As a result, the accuracy of the Bagging (Decision Tree) is 99.71% and Bagging (Gaussian Naïve Bayes) is 67.57%.
KW - Accuracy
KW - Anomaly-Based Intrusion Detection System
KW - CIDDS
KW - Detection Rate
KW - Ensemble Methods
UR - http://www.scopus.com/inward/record.url?scp=85123283087&partnerID=8YFLogxK
U2 - 10.1109/ICTS52701.2021.9608714
DO - 10.1109/ICTS52701.2021.9608714
M3 - Conference contribution
AN - SCOPUS:85123283087
T3 - Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021
SP - 62
EP - 67
BT - Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Information and Communication Technology and System, ICTS 2021
Y2 - 20 October 2021 through 21 October 2021
ER -