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Alile S. O.

Work place: epartment of Computer Science University of Benin, Benin City, Edo State, Nigeria

E-mail: solomon.alile@physci.uniben.edu

Website:

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

Biography

Alile Solomon Osarumwense obtained his Diploma in Data Processing degree from University of Benin in 2004, B.Sc. degree in Computer Science in 2012 from Lagos State University (LASU), Ojo, Lagos and M.Sc in Computer Science from University of Benin in 2019. He is a Cisco Certified Network Associate (Routing and Switching) and System Engineer. His area of interest includes Information Technology, Soft Computing, Machine Learning and Cybersecurity. He is currently conducting research works in the area of cybersecurity. He is a member of International Computer Science and Engineering Society (ICSES), Institute For Engineering Research and Publication (IFERP), International Association of Engineers (IAENG), International Association of Engineers Society of Computer Science (ISCS), International Association of Engineers Society of Wireless Networks (ISWN), International Association of Engineers

Society of Scientific Computing (ISSC), International Association of Engineers Society of Internet Computing and Web Services (ISICWS), International Association of Engineers Society of Information System Engineering (ISISE), International Association of Engineers Society of Data Mining (ISDM), International Association of Engineers Society of Artificial Intelligence (ISAI), and International Association of Engineers Society of Software Engineering (ISSE).

Author Articles
A Bayesian Belief Network Model For Detecting Multi-stage Attacks With Malicious IP Addresses

By Alile S. O. Egwali A. O.

DOI: https://doi.org/10.5815/ijwmt.2020.02.04, Pub. Date: 8 Apr. 2020

Multi-stage attacks are attacks executed in phases where each phase of the attack solely relies on the completion of the preceding phase.  These attacks are so intelligently designed that they are able to elude detection from most network instruction detection systems and they are capable of penetrating sophisticated defenses.  In this paper, we proposed and simulated a Bayesian Belief Network Model to predict Multi-stage Attacks with Malicious IP.  The model was designed using Bayes Server and tested with data collected from cyber security repository.  The model had a 99% prediction accuracy. 

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