A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling
Conference Proceeding
Turnbull, L., Tan, Z., & Babaagba, K. (2022)
A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling. In 2022 IEEE Conference on Dependable and Secure Computing (DSC). https://doi.org/10.1109/DSC54232.2022.9888906
Malicious software trends show a persistent yearly increase in volume and cost impact. More than 350,000 new malicious or unwanted programs that target various technologies we...
A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs
Conference Proceeding
McLaren, R. A., Babaagba, K., & Tan, Z. (in press)
A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs. In The 8th International Conference on machine Learning, Optimization and Data science - LOD 2022
As the field of malware detection continues to grow, a shift in focus is occurring from feature vectors and other common, but easily obfuscated elements to a semantics based a...
Applications of Evolutionary Computation: 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings
Book
Jiménez Laredo, J. L., Hidalgo, J. I., & Babaagba, K. O. (Eds.)
(2022). Applications of Evolutionary Computation: 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings. Cham: Springer. https://doi.org/10.1007/978-3-031-02462-7
This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2022, held as part of Evo*2022...
Toward Machine Intelligence that Learns to Fingerprint Polymorphic Worms in IoT
Journal Article
Wang, F., Yang, S., Wang, C., Li, Q., Babaagba, K., & Tan, Z. (2022)
Toward Machine Intelligence that Learns to Fingerprint Polymorphic Worms in IoT. International Journal of Intelligent Systems, 37(10), 7058-7078. https://doi.org/10.1002/int.22871
Internet of Things (IoT) is fast growing. Non-PC devices under the umbrella of IoT have been increasingly applied in various fields and will soon account for a significant sha...
Application of evolutionary machine learning in metamorphic malware analysis and detection
Thesis
Babaagba, K. O. Application of evolutionary machine learning in metamorphic malware analysis and detection. (Thesis)
Edinburgh Napier University. Retrieved from http://researchrepository.napier.ac.uk/Output/2801469
In recent times, malware detection and analysis are becoming key issues. A dangerous class of malware is metamorphic malware which is capable of modifying its own code and hid...
Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples
Conference Proceeding
Babaagba, K., Tan, Z., & Hart, E. (2020)
Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples. https://doi.org/10.1109/CEC48606.2020.9185668
Detecting metamorphic malware provides a challenge to machine-learning models as trained models might not generalise to future mutant variants of the malware. To address this,...
Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites
Conference Proceeding
Babaagba, K. O., Tan, Z., & Hart, E. (2020)
Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites. In Applications of Evolutionary Computation. EvoApplications 2020. , (117-132). https://doi.org/10.1007/978-3-030-43722-0_8
In the field of metamorphic malware detection, training a detection model with malware samples that reflect potential mutants of the malware is crucial in developing a model r...
Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme
Conference Proceeding
Babaagba, K. O., Tan, Z., & Hart, E. (2019)
Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme. In Dependability in Sensor, Cloud, and Big Data Systems and Applications. , (369-382). https://doi.org/10.1007/978-981-15-1304-6_29
The ability to detect metamorphic malware has generated significant research interest over recent years, particularly given its proliferation on mobile devices. Such malware i...
A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning
Conference Proceeding
Babaagba, K. O., & Adesanya, S. O. (2019)
A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning. In ICEIT 2019: Proceedings of the 2019 8th International Conference on Educational and Information Technology (51–55). https://doi.org/10.1145/3318396.3318448
In this paper, the effect of feature selection in malware detection using machine learning techniques is studied. We employ supervised and unsupervised machine learning algori...