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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Anomaly Detection using Network Metadata

Author 1: Khaled Mutmbak
Author 2: Sultan Alotaibi
Author 3: Khalid Alharbi
Author 4: Umar Albalawi
Author 5: Osama Younes

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130593

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 5, 2022.

  • Abstract and Keywords
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Abstract: The proliferation of numerous network function today gave rise to the importance of network traffic classification against various cyber-attacks. Automatic training with a huge number of representative data necessitates the creation of a model for an efficient classifier. As a result, automatic categorization requires using training techniques capable of assigning classes to data objects based on the activities supplied to learn classes. Predefined classes allow for the detection of new items. However, the analysis and categorization of data activity in intrusion detection systems are vulnerable to a wide range of threats. Thus, New methods of analysis must be developed in order to establish an appropriate approach for monitoring circulating traffic in order to solve this problem. The major goal of this research is to develop and verify a heterogeneous traffic classifier that can classify the collected metadata of networks. In this study, a new model is proposed, which is based on machine learning technique, to increase the accuracy of prediction. Prior to the analysis stage, the gathered traffic is subjected to preprocessing. This paper aims to provide the mathematical validation of a novel machine learning classifier for heterogeneous traffic and anomaly detection.

Keywords: Anomaly detection; network metadata; packet anal-ysis; intrusion detection system; machine learning; classification; heterogeneous traffic

Khaled Mutmbak, Sultan Alotaibi, Khalid Alharbi, Umar Albalawi and Osama Younes, “Anomaly Detection using Network Metadata” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130593

@article{Mutmbak2022,
title = {Anomaly Detection using Network Metadata},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130593},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130593},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Khaled Mutmbak and Sultan Alotaibi and Khalid Alharbi and Umar Albalawi and Osama Younes}
}


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