Distributed Denial of Service Detection using Multi Layered Feed Forward Artificial Neural Network

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Ismaila Idris 1,* Obi Blessing Fabian 1 Shafii M. Abdulhamid 1 Morufu Olalere 1 Baba Meshach 1

1. Department of Cyber Security, Federal University of Technology, Minna, Nigeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2017.12.04

Received: 20 Jun. 2017 / Revised: 26 Jul. 2017 / Accepted: 10 Aug. 2017 / Published: 8 Dec. 2017

Index Terms

DDoS attacks, DDoS detectors, Artificial Neural Network, Feed Forward Artificial Neural Network


One of the dangers faced by various organizations and institutions operating in the cyberspace is Distributed Denial of Service (DDoS) attacks; it is carried out through the internet. It resultant consequences are that it slow down internet services, makes it unavailable, and sometime destroy the systems. Most of the services it affects are online applications and procedures, system and network performance, emails and other system resources. The aim of this work is to detect and classify DDoS attack traffics and normal traffics using multi layered feed forward (FFANN) technique as a tool to develop model. The input parameters used for training the model are: service count, duration, protocol bit, destination byte, and source byte, while the output parameters are DDoS attack traffic or normal traffic. KDD99 dataset was used for the experiment. After the experiment the following results were gotten, 100% precision, 100% specificity rate, 100% classified rate, 99.97% sensitivity. The detection rate is 99.98%, error rate is 0.0179%, and inconclusive rate is 0%. The results above showed that the accuracy rate of the model in detecting DDoS attack is high when compared with that of the related works which recorded detection accuracy as 98%, sensitivity 96%, specificity 100% and precision 100%.

Cite This Paper

Ismaila Idris, Obi Blessing Fabian, Shafi’i M. Abdulhamid, Morufu Olalere, Baba Meshach, "Distributed Denial of Service Detection using Multi Layered Feed Forward Artificial Neural Network", International Journal of Computer Network and Information Security(IJCNIS), Vol.9, No.12, pp.29-35, 2017. DOI:10.5815/ijcnis.2017.12.04


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