Morufu Olalere

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



Research Interests: Information Security, Network Architecture, Network Security, Information Systems, Control Theory


Morufu Olalere is a lecturer in the Department of Cyber Security Science, Federal University of Technology Minna, Niger State, Nigeria. He graduated in 2005 from the Department of Industrial Mathematics and Computer Science of the Federal University of Technology Akure, Nigeria with Bachelor of Technology in Industrial Mathematics. He bagged MSc. in Computer science from the University of Ilorin, Kwara State, Nigeria in 2011. He completed his PhD in Security in computing in 2016 from the Faculty of Computer Science and Information Technology of the University Putra Malaysia, Selangor, Malaysia. He has a number of professional certifications including OCH, CWSA and CWSP. He is a member of the following professional bodies; The Computer Professionals Registration Council of Nigeria (CPN), The Nigeria Computer Society (NCS), The Institute of Electrical and Electronics Engineers (IEEE) Computer Society, and The Association for Information Systems (AIS). His current research interests include: Access control, Biometrics, Information Security, and Network Security.

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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