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.

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},
}