Fagbola Temitayo
Lecturer, Department of Computer Science, Faculty of Science, Federal University, Oye-Ekiti, Ekiti State, Nigeria.
PhD (Computer Science), MSc (Cybersecurity), BTech (Hons) in Computer Science, MNCS, IAENG, OCA, MSAB.
[email protected]
Supervisors: Prof. E. O. Omidiora, Prof S. O. Olabiyisi, and Prof Olumide Longe
Phone: +234-703-0513-010
Address: Department of Computer science, Federal University, Oye-Ekiti, Ekiti State, Nigeria.
PhD (Computer Science), MSc (Cybersecurity), BTech (Hons) in Computer Science, MNCS, IAENG, OCA, MSAB.
[email protected]
Supervisors: Prof. E. O. Omidiora, Prof S. O. Olabiyisi, and Prof Olumide Longe
Phone: +234-703-0513-010
Address: Department of Computer science, Federal University, Oye-Ekiti, Ekiti State, Nigeria.
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Papers by Fagbola Temitayo
demonstrates its superiority over the two hybridized algorithms in terms of their simulation time and software complexity
measurement when used to solve a typical University Examination Timetabling Problem (ETP).Preparation of a timetable
consists basically of allocating a number of events to a finite number of time periods (also called slots) in such a way that a
certain set of constraints is satisfied. The developed model was used to schedule the first semester examination of Ladoke
Akintola University of Technology, Ogbomoso Nigeria during the 2010/2011 session. A task involving 20,100 students,
652 courses, 52 examination venues for 17days excluding Saturdays and Sundays.The use of the implemented model
resulted in significant time savings in the scheduling of the timetable, a shortening of the examination period and a well
spread examination for the students. Also, none of the lecturers / examination invigilators was double booked or booked
successively. It was clearly evident that the hybrid model outperformed Simulated Annealing and Genetic Algorithm in
most of the evaluated parameters.
the mobile enterprise led to the development of new breed of
security systems known as mobile intrusion detection system.
The major worry of mobile / ubiquitous device users is the issue
of data security since no mobile security application is 100%
efficient. Existing studies conducted on android mobile security
reveal that Android is the platform with the highest malware
growth rate by the end of 2011 and that Global System for
Mobile Communication -based Pivot Attacks, Mobile Botnets
and Malicious Applications are the major security vulnerabilities
compromising the confidentiality, integrity and availability of
this mobile enterprise. In this paper, a SwarmDroid IDS is
developed following a machine learning approach using Support
Vector Machine. NSL-KDD dataset was used to test and evaluate
the performance of the SwarmDroid IDS and compared with J48
and Random Forest which are state-of-the-art machine learning
techniques for intrusion detection in mobiles. Particle Swarm
Optimization was used for feature selection. The malware
detection systems were simulated in a MATLAB environment.
The SwarmDroid IDS was evaluated using detection time, true
positive rate, false positive rate and detection accuracy as
performance metrics. The result obtained from the evaluation
revealed that SwarmDroid IDS outperforms J48 in terms of
detection time and accuracy. Also, feature selection in Android
application package files using particle swarm optimization
technique plays a critical role in realizing high accuracy and low
computational time complexity in SwarmDroid.
Teaching Documents by Fagbola Temitayo
demonstrates its superiority over the two hybridized algorithms in terms of their simulation time and software complexity
measurement when used to solve a typical University Examination Timetabling Problem (ETP).Preparation of a timetable
consists basically of allocating a number of events to a finite number of time periods (also called slots) in such a way that a
certain set of constraints is satisfied. The developed model was used to schedule the first semester examination of Ladoke
Akintola University of Technology, Ogbomoso Nigeria during the 2010/2011 session. A task involving 20,100 students,
652 courses, 52 examination venues for 17days excluding Saturdays and Sundays.The use of the implemented model
resulted in significant time savings in the scheduling of the timetable, a shortening of the examination period and a well
spread examination for the students. Also, none of the lecturers / examination invigilators was double booked or booked
successively. It was clearly evident that the hybrid model outperformed Simulated Annealing and Genetic Algorithm in
most of the evaluated parameters.
the mobile enterprise led to the development of new breed of
security systems known as mobile intrusion detection system.
The major worry of mobile / ubiquitous device users is the issue
of data security since no mobile security application is 100%
efficient. Existing studies conducted on android mobile security
reveal that Android is the platform with the highest malware
growth rate by the end of 2011 and that Global System for
Mobile Communication -based Pivot Attacks, Mobile Botnets
and Malicious Applications are the major security vulnerabilities
compromising the confidentiality, integrity and availability of
this mobile enterprise. In this paper, a SwarmDroid IDS is
developed following a machine learning approach using Support
Vector Machine. NSL-KDD dataset was used to test and evaluate
the performance of the SwarmDroid IDS and compared with J48
and Random Forest which are state-of-the-art machine learning
techniques for intrusion detection in mobiles. Particle Swarm
Optimization was used for feature selection. The malware
detection systems were simulated in a MATLAB environment.
The SwarmDroid IDS was evaluated using detection time, true
positive rate, false positive rate and detection accuracy as
performance metrics. The result obtained from the evaluation
revealed that SwarmDroid IDS outperforms J48 in terms of
detection time and accuracy. Also, feature selection in Android
application package files using particle swarm optimization
technique plays a critical role in realizing high accuracy and low
computational time complexity in SwarmDroid.