Solomon A. Akinboro

Work place: Bells University of Technology/ Department of Computer Science and Information Technology, Ota, Nigeria



Research Interests: Computational Science and Engineering, Computer systems and computational processes, Computer Architecture and Organization, Network Architecture, Network Security, Distributed Computing, Data Structures and Algorithms


Solomon A. Akinboro is a Senior Lecturer from Bells University of Technology, Ota, Ogun state, Nigeria, holds a B. Tech degree in Computer Engineering from Ladoke Akintola University of Technology, Ogbomosho, M.Sc. and PhD in Computer science from Obafemi Awolowo University Ile-Ife. Research interests include Distributed system and computer network, Mobile Computing and Machine Learning. Member of the following professional bodies: Nigeria Computer Society, Nigeria Society of Engineers and Council for the Regulation of Engineering in Nigeria

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

By Solomon A. Akinboro Adebayo Omotosho Modupe O. Odusami

DOI:, 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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