TY - GEN
T1 - Comparative Analysis of Novelty Detection Algorithms in Network Intrusion Detection Systems
AU - Halim, Maxmilian
AU - Pratomo, Baskoro Adi
AU - Jati Santoso, Bagus
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Network intrusion detection is a crucial task in ensuring the security and reliability of computer networks. In recent years, machine learning algorithms have shown promising results in identifying anomalous activities indicative of network intrusions. In the context of intrusion detection systems, novelty detection often receives limited attention within machine learning communities. This oversight can be attributed to the historical emphasis on optimizing performance metrics using established datasets, which may not adequately represent the evolving landscape of cyber threats. This research aims to compare four widely used novelty detection algorithms for network intrusion detection, namely SGDOneClassSVM, LocalOutlierDetection, EllipticalEnvelope Covariance, and Isolation Forest. Our experiments with the UNSW-NB15 dataset show that Isolation Forest was the best-performing algorithm with an F1-score of 0.723. The result shows that network-based intrusion detection systems are still challenging for novelty detection algorithms.
AB - Network intrusion detection is a crucial task in ensuring the security and reliability of computer networks. In recent years, machine learning algorithms have shown promising results in identifying anomalous activities indicative of network intrusions. In the context of intrusion detection systems, novelty detection often receives limited attention within machine learning communities. This oversight can be attributed to the historical emphasis on optimizing performance metrics using established datasets, which may not adequately represent the evolving landscape of cyber threats. This research aims to compare four widely used novelty detection algorithms for network intrusion detection, namely SGDOneClassSVM, LocalOutlierDetection, EllipticalEnvelope Covariance, and Isolation Forest. Our experiments with the UNSW-NB15 dataset show that Isolation Forest was the best-performing algorithm with an F1-score of 0.723. The result shows that network-based intrusion detection systems are still challenging for novelty detection algorithms.
KW - NIDS
KW - machine learning
KW - novelty detection
UR - http://www.scopus.com/inward/record.url?scp=85186494414&partnerID=8YFLogxK
U2 - 10.1109/ICAMIMIA60881.2023.10427625
DO - 10.1109/ICAMIMIA60881.2023.10427625
M3 - Conference contribution
AN - SCOPUS:85186494414
T3 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
SP - 306
EP - 310
BT - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023
Y2 - 14 November 2023 through 15 November 2023
ER -
