Searching for optimal machine learning algorithm for network traffic classification in intrusion detection system

Searching for optimal machine learning algorithm for network traffic classification in intrusion detection system

The main problem associated with the development of an effective network behaviour anomaly detection-based IDS model is the selection of the optimal network traffic classification method. This article presents the results of simulation research on the effectiveness of the use of machine learning alg...

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Bibliographic Details
Main Author: Gerka Alicja
Format: Article
Language:English
Published: EDP Sciences 2018-01-01
Series:ITM Web of Conferences
Online Access:https://doi.org/10.1051/itmconf/20182100027
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spelling doaj-17e41eef4777410db3595864fdbee36c2021-02-02T07:02:04ZengEDP SciencesITM Web of Conferences2271-20972018-01-01210002710.1051/itmconf/20182100027itmconf_cst2018_00027Searching for optimal machine learning algorithm for network traffic classification in intrusion detection systemGerka Alicja0Rzeszow University of Technology, The Faculty of Electrical and Computer EngineeringThe main problem associated with the development of an effective network behaviour anomaly detection-based IDS model is the selection of the optimal network traffic classification method. This article presents the results of simulation research on the effectiveness of the use of machine learning algorithms in the network attacks detection. The research part of the work concerned finding the optimal method of network packets classification possible to implement in the intrusion detection system’s attack detection module. During the research, the performance of three machine learning algorithms (Artificial Neural Network, Support Vector Machine and Naïve Bayes Classifier) has been compared using a dataset from the KDD Cup competition. Attention was also paid to the relationship between the values of algorithm parameters and their effectiveness. The work also contains an short analysis of the state of cybersecurity in Poland.https://doi.org/10.1051/itmconf/20182100027
collection DOAJ
language English
format Article
sources DOAJ
author Gerka Alicja
spellingShingle Gerka Alicja
Searching for optimal machine learning algorithm for network traffic classification in intrusion detection system
ITM Web of Conferences
author_facet Gerka Alicja
author_sort Gerka Alicja
title Searching for optimal machine learning algorithm for network traffic classification in intrusion detection system
title_short Searching for optimal machine learning algorithm for network traffic classification in intrusion detection system
title_full Searching for optimal machine learning algorithm for network traffic classification in intrusion detection system
title_fullStr Searching for optimal machine learning algorithm for network traffic classification in intrusion detection system
title_full_unstemmed Searching for optimal machine learning algorithm for network traffic classification in intrusion detection system
title_sort searching for optimal machine learning algorithm for network traffic classification in intrusion detection system
publisher EDP Sciences
series ITM Web of Conferences
issn 2271-2097
publishDate 2018-01-01
description The main problem associated with the development of an effective network behaviour anomaly detection-based IDS model is the selection of the optimal network traffic classification method. This article presents the results of simulation research on the effectiveness of the use of machine learning algorithms in the network attacks detection. The research part of the work concerned finding the optimal method of network packets classification possible to implement in the intrusion detection system’s attack detection module. During the research, the performance of three machine learning algorithms (Artificial Neural Network, Support Vector Machine and Naïve Bayes Classifier) has been compared using a dataset from the KDD Cup competition. Attention was also paid to the relationship between the values of algorithm parameters and their effectiveness. The work also contains an short analysis of the state of cybersecurity in Poland.
url https://doi.org/10.1051/itmconf/20182100027
work_keys_str_mv AT gerkaalicja searchingforoptimalmachinelearningalgorithmfornetworktrafficclassificationinintrusiondetectionsystem
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