Obi Blessing Fabian

Work place: Department of Cyber Security, Federal University of Technology, Minna, Nigeria



Research Interests: Application Security, Hardware Security, Information Security, Network Security, Information-Theoretic Security


Obi Blessing Fabian is a Masters student in the Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria. She obtained her Bachelor degree in Computer Science in the same University with Second Class Upper. She did her National Youth Service Corp (NYSC) in Niger State School of Health Technology as a lecturer, currently she is working as a teacher in NECO Staff School Minna, Niger State Nigeria. Obi obtained her First School Living Certificate from Aliyu Mustapha Academy Yola, Adamawa State, Nigeria in the year 1999, Senior School Certificate and West Africa Senior School Certificate from Federal Government Girls’ College Yola, Adamawa state Nigeria.

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

By Ismaila Idris Obi Blessing Fabian Shafii M. Abdulhamid Morufu Olalere Baba Meshach

DOI:, Pub. Date: 8 Dec. 2017

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%.

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