Adebayo Omotosho

Work place: Landmark University/ Department of Computer Science, Omu-Aran, Nigeria

E-mail: bayotosho@gmail.com

Website:

Research Interests: Medical Informatics, Computer systems and computational processes, Computer Networks, Information Security, Network Security

Biography

Adebayo Omotosho received his PhD in Computer Science at Ladoke Akintola University of Technology in 2016. He is a Seasoned Computer Programmer and has taken part in a number of programming competitions in C/C++/C#. He is a member of the Nigeria Computer Society (NCS), Computer Professional [Registration Council] of Nigeria (CPN), Computer Science Teachers Association for Computing Machinery (ACM), and International Association of Computer Science and Information Technology. His research interests are health informatics, computer security, machine learning and biometrics.

Author Articles
An Improved Model for Securing Ambient Home Network against Spoofing Attack

By Solomon A. Akinboro Adebayo Omotosho Modupe O. Odusami

DOI: https://doi.org/10.5815/ijcnis.2018.02.03, Pub. Date: 8 Feb. 2018

Mobile Ad hoc Networks (MANET) are prone to malicious attacks and intermediate nodes on the home network may spoof the packets being transmitted before reaching the destination. This study implements an enhanced Steganography Adaptive Neuro-Fuzzy Algorithm (SANFA) technique for securing the ambient home network against spoofing attacks. Hybrid techniques that comprises image steganography, adaptive neuro-fuzzy and transposition cipher were used for the model development. Two variant of the model: SANFA and transpose SANFA were compared using precision and convergence time as performance metrics. The simulation results showed that the transpose SANFA has lower percentage of precision transmitting in a smaller network and a higher percentage of precision transmitting in a larger network. The convergence time result showed that packet transmitted in a smaller network size took longer time to converge while packet transmitted in a larger network size took shorter period to converge.

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