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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.

Intrusion Detection System based on the SDN Network, Bloom Filter and Machine Learning

Author 1: Traore Issa
Author 2: Kone Tiemoman

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2019.0100953

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 9, 2019.

  • Abstract and Keywords
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Abstract: The scale and frequency of sophisticated attacks through denial of distributed service (DDoS) are still growing. The urgency is required because with the new emerging paradigms of the Internet of Things (IoT) and Cloud Computing, billions of unsecured connected objects will be available. This document deals with the detection, and correction of DDoS attacks based on real-time behavioral analysis of traffic. This method is based on Software Defined Network (SDN) technologies, Bloom filter and automatic behaviour learning. Indeed, distributed denial of service attacks (DDoS) are difficult to detect in real time. In particular, it concerns the distinction between legitimate and illegitimate packages. Our approach outlines a supervised classification method based on Machine Learning that identifies malicious and normal packets. Thus, we design and implement Defined (IDS) with a great precision. The results of the evaluation suggest that our proposal is timely and detects several abnormal DDoS-based cyber-attack behaviours.

Keywords: Distributed denial of service; intrusion detection software; software defined network; machine learning; synchronize; acknowledgment; bloom filter

Traore Issa and Kone Tiemoman, “Intrusion Detection System based on the SDN Network, Bloom Filter and Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 10(9), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100953

@article{Issa2019,
title = {Intrusion Detection System based on the SDN Network, Bloom Filter and Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100953},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100953},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {9},
author = {Traore Issa and Kone Tiemoman}
}


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