All Outputs (6)

Emotion Recognition on Social Media Using Natural Language Processing (NLP) Techniques (2023)
Presentation / Conference Contribution
Gomez, L. R., Watt, T., Babaagba, K. O., Chrysoulas, C., Homay, A., Rangarajan, R., & Liu, X. (2023). Emotion Recognition on Social Media Using Natural Language Processing (NLP) Techniques. In ICISS '23: Proceedings of the 2023 6th International Conferen

In recent years, text has been the main form of communication on social media platforms such as Twitter, Reddit, Facebook, Instagram and YouTube. Emotion Recognition from these platforms can be exploited for all sorts of applications. Through the mea... Read More about Emotion Recognition on Social Media Using Natural Language Processing (NLP) Techniques.

A Generative Neural Network for Improving Metamorphic Malware Detection in IoT Mobile Devices (2023)
Book Chapter
Turnbull, L., Tan, Z., & Babaagba, K. O. (2024). A Generative Neural Network for Improving Metamorphic Malware Detection in IoT Mobile Devices. In A. Ismail Awad, A. Ahmad, K. Raymond Choo, & S. Hakak (Eds.), Internet of Things Security and Privacy: Pract

There has been an upsurge in malicious attacks in recent years, impacting computer systems and networks. More and more novel malware families aimed at information assets were launched daily over the past year. A particularly threatening malicious gro... Read More about A Generative Neural Network for Improving Metamorphic Malware Detection in IoT Mobile Devices.

Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices (2023)
Presentation / Conference Contribution
Spalding, A., Tan, Z., & Babaagba, K. O. (2023, November). Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices. Presented at The International Symposium on Intelligent and Trustworthy Comp

Data recovery for forensic analysis of both hard drives and solid state media presents its own unique set of challenges. Hard drives face mechanical failures and data fragmentation , but their sequential storage and higher success rates make recovery... Read More about Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices.

An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware (2023)
Presentation / Conference Contribution
Babaagba, K. O., & Wylie, J. (2023). An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware. In GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation (1753-17

Defeating dangerous families of malware like polymorphic and metamorphic malware have become well studied due to their increased attacks on computer systems and network. Traditional Machine Learning (ML) models have been used in detecting this malwar... Read More about An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware.

Evolutionary based Transfer Learning Approach to Improving Classification of Metamorphic Malware (2023)
Presentation / Conference Contribution
Babaagba, K. O., & Ayodele, M. (2023). Evolutionary based Transfer Learning Approach to Improving Classification of Metamorphic Malware. In Applications of Evolutionary Computation – 26th International Conference, EvoApplications 2023 (161-176). https:

The proliferation of metamorphic malware has recently gained a lot of research interest. This is because of their ability to transform their program codes stochastically. Several detectors are unable to detect this malware family because of how quick... Read More about Evolutionary based Transfer Learning Approach to Improving Classification of Metamorphic Malware.

A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs (2023)
Presentation / Conference Contribution
McLaren, R. A., Babaagba, K., & Tan, Z. (2023). A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs. In Machine Learning, Optimization, and Data Science: 8th International Conference, LOD 2022, Certosa di Po

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 approach. This is due to the emergence of more complex malware families that... Read More about A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs.