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Comparative Evaluation of Machine Learning Algorithms for Network Intrusion Detection and Attack Classification
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3425-3837
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-5269-3900
2022 (English)In: Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc. , 2022, Vol. 2022-July, article id 183333Conference paper, Published paper (Refereed)
Abstract [en]

With the increasing use of the internet and reliance on computer-based systems for our daily lives, any vulnerability in those systems is one of the most important issues for the community. For this reason, the need for intelligent models that detect malicious intrusions is important to keep our personal information safe. In this paper, we investigate several supervised (Artificial Neural Network, Support Vector Machine, Random Forest, Linear Discriminant Analysis, and K-Nearest Neighbors) and unsupervised (K-means, Mean-shift, and DBSCAN) machine learning algorithms, in the context of anomaly-based Intrusion Detection Systems. We are using four different IDS benchmark datasets (KDD99, NSL-KDD, UNSW-NB15, and CIC-IDS-2017) to evaluate the performance of the selected machine learning algorithms for both intrusion detection and attack classification. The results have shown that Random Forest is the most suitable algorithm regarding model accuracy and execution time.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2022. Vol. 2022-July, article id 183333
Keywords [en]
Attack Classification, Intrusion Detection, Machine Learning, Supervised Learning, Unsupervised Learning, Benchmarking, Decision trees, Discriminant analysis, K-means clustering, Nearest neighbor search, Network security, Neural networks, Support vector machines, Attack classifications, Comparative evaluations, Computer-based system, Daily lives, Intelligent models, Intrusion-Detection, Machine learning algorithms, Machine-learning, Network intrusion detection, Random forests
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-60964DOI: 10.1109/IJCNN55064.2022.9892293ISI: 000867070903053Scopus ID: 2-s2.0-85136327314ISBN: 9781728186719 (print)OAI: oai:DiVA.org:mdh-60964DiVA, id: diva2:1712568
Conference
2022 International Joint Conference on Neural Networks, IJCNN 2022, Padua, Italy, 18-23 July 2022
Available from: 2022-11-22 Created: 2022-11-22 Last updated: 2023-04-19Bibliographically approved

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Leon, MiguelMarkovic, TijanaPunnekkat, Sasikumar

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