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research-article

Comparison of Machine Learning Algorithms for Detection of Network Intrusions

Authors:

Ljiljana TrajkovicAuthors Info & Claims

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Pages 4248 - 4253

https://doi.org/10.1109/SMC.2018.00719

Published: 07 October 2018 Publication History

Abstract

Detecting, analyzing, and defending against network intrusions is an important topic in cyber security. Various detection systems have been designed using machine learning techniques that help detect malicious intentions of network users. We apply Recurrent Neural Networks (RNNs) and Broad Learning System (BLS) machine learning algorithms to classify known network intrusions. The developed models are trained and tested using the NSL-KDD dataset containing information about both intrusion and regular network connections. The algorithms are used to classify various types of intrusion classes and regular data and are compared based on accuracy and F-Score. Comparison results indicate that the BLS algorithm shows comparable performance with shorter training time.

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Cited By

Faiaz MMitra DDas Prangon R(2024)Intrusion Detection Using Convolutional Neural Network: A Color Mapping Approach on NSL-KDD DatasetProceedings of the 11th International Conference on Networking, Systems, and Security10.1145/3704522.3704541(154-162)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3704522.3704541
Saheed YMisra S(2024)A voting gray wolf optimizer-based ensemble learning models for intrusion detection in the Internet of ThingsInternational Journal of Information Security10.1007/s10207-023-00803-x23:3(1557-1581)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10207-023-00803-x
Yang LYang ZZheng ZHe LLi FChen C(2022)Anomaly Detection based on Broad Leaning System for Rolling Element Bearing Fault DiagnosisAdjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers10.1145/3544793.3560408(462-467)Online publication date: 11-Sep-2022
https://dl.acm.org/doi/10.1145/3544793.3560408
Show More Cited By

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Comparison of Machine Learning Algorithms for Detection of Network Intrusions

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cover image Guide Proceedings

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Oct 2018

4300 pages

Copyright © 2018.

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IEEE Press

Publication History

Published: 07 October 2018

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Cited By

Faiaz MMitra DDas Prangon R(2024)Intrusion Detection Using Convolutional Neural Network: A Color Mapping Approach on NSL-KDD DatasetProceedings of the 11th International Conference on Networking, Systems, and Security10.1145/3704522.3704541(154-162)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3704522.3704541
Saheed YMisra S(2024)A voting gray wolf optimizer-based ensemble learning models for intrusion detection in the Internet of ThingsInternational Journal of Information Security10.1007/s10207-023-00803-x23:3(1557-1581)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10207-023-00803-x
Yang LYang ZZheng ZHe LLi FChen C(2022)Anomaly Detection based on Broad Leaning System for Rolling Element Bearing Fault DiagnosisAdjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers10.1145/3544793.3560408(462-467)Online publication date: 11-Sep-2022
https://dl.acm.org/doi/10.1145/3544793.3560408
Jara-Maldonado MAlarcon-Aquino VRosas-Romero R(2022)A new machine learning model based on the broad learning system and waveletsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.104886112:COnline publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1016/j.engappai.2022.104886

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