Kehinde Oluwatoyin Babaagba, “Application of Evolutionary Machine Learning in Metamorphic Malware Analysis and Detection,” PhD Thesis, Edinburgh Napier University, Edinburgh, UK, 2020.


K.O. Babaagba, Z. Tan, and E. Hart, “Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples,” in 2020 IEEE World Congress on Computational Intelligence, Glasgow, UK, 2020.

F. Wang, S. Yang, C. Wang, Q. Li, K.O. Babaagba, and Z. Tan (in press), Toward Machine Intelligence that Learns to Fingerprint Polymorphic Worms in IoT, International Journal of Intelligent Systems.

K.O. Babaagba, Z. Tan, and E. Hart, “Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites,” in 23rd European Conference on the Applications of Evolutionary and bio-inspired Computation, P.A. Castillo et al, Ed. Seville: Springer-Verlag New York, Inc., 2020, pp. 1–16. Awarded Outstandind Student Contribution and Best Student Paper.


K. O. Babaagba, Z. Tan, and E. Hart, “Nowhere metamorphic malware can hide - a biological evolution inspired detection scheme,” in Dependability in Sensor, Cloud, and Big Data Systems and Applications, G. Wang, M. Z. A. Bhuiyan, S. De Capitani di Vimercati, and Y. Ren, Eds. Singapore: Springer Singapore, 2019, pp. 369–382.

K. O. Babaagba and S. O. Adesanya, “A study on the effect of feature selection on malware analysis using machine learning,” in ACM 8th International Conference on Educational and Information Technology, 2019, pp. 51-55. Awarded Excellent Oral Presentation.


K. O. Babaagba and S. A. Arekete, “A Design of an Agent Based System for Timetabling,” Int. J. Eng. Dev. Res., vol. 5, no. 2, pp. 1168–1175, 2017.

K. O. Babaagba and S. A. Arekete, “A Review of Agent Based University Course Time Tabling Systems,” Int. J. Eng. Dev. Res., vol. 5, no. 2, pp. 566–568, 2017.