All Outputs (13)

Exploring the use of fitness landscape analysis for understanding malware evolution (2024)
Presentation / Conference Contribution
Babaagba, K., Murali, R., & Thomson, S. L. (2024, July). Exploring the use of fitness landscape analysis for understanding malware evolution. Presented at ACM Genetic and Evolutionary Computation Conference (GECCO) 2024, Melbourne, Australia

We conduct a preliminary study exploring the potential of using fitness landscape analysis for understanding the evolution of malware. This type of optimisation is fairly new and has not previously been studied through the lens of landscape analysis.... Read More about Exploring the use of fitness landscape analysis for understanding malware evolution.

Image Forgery Detection using Cryptography and Deep Learning (2024)
Presentation / Conference Contribution
Oke, A., & Babaagba, K. O. (2024). Image Forgery Detection using Cryptography and Deep Learning. In Big Data Technologies and Applications. BDTA 2023 (62-78). https://doi.org/10.1007/978-3-031-52265-9_5

The advancement of technology has undoubtedly exposed everyone to a remarkable array of visual imagery. Nowadays, digital technology is eating away the trust and historical confidence people have in the integrity of imagery. Deep learning is often us... Read More about Image Forgery Detection using Cryptography and Deep Learning.

Can Federated Models Be Rectified Through Learning Negative Gradients? (2024)
Presentation / Conference Contribution
Tahir, A., Tan, Z., & Babaagba, K. O. Can Federated Models Be Rectified Through Learning Negative Gradients?. Presented at 13th EAI International Conference, BDTA 2023, Edinburgh

Federated Learning (FL) is a method to train machine learning (ML) models in a decentralised manner, while preserving the privacy of data from multiple clients. However, FL is vulnerable to malicious attacks, such as poisoning attacks, and is challen... Read More about Can Federated Models Be Rectified Through Learning Negative Gradients?.

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 Computing, Communications, and Networking (ITCCN-2023), Exeter, UK

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.

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, August). Emotion Recognition on Social Media Using Natural Language Processing (NLP) Techniques. Presented at ICISS 2023: The 6th International Conference on Information Science and Systems, Edinburgh

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.

An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware (2023)
Presentation / Conference Contribution
Babaagba, K. O., & Wylie, J. (2023, July). An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware. Presented at The Genetic and Evolutionary Computation Conference (GECCO) 2023, Lisbon

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, April). Evolutionary based Transfer Learning Approach to Improving Classification of Metamorphic Malware. Presented at EvoApplications 2023: 26th International Conference on the Applications of Evolutionary Computation, Brno, Czech Republic

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. (2022, September). A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs. Presented at The 8th International Conference on machine Learning, Optimization and Data science - LOD 2022, Certosa di Pontignano, Siena – Tuscany, Italy

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.

A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling (2022)
Presentation / Conference Contribution
Turnbull, L., Tan, Z., & Babaagba, K. (2022, June). A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling. Presented at The 2022 5th IEEE Conference on Dependable and Secure Computing (IEEE DSC 2022), Edinburgh [Online]

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 were registered daily over the past year. Metamorphic malware is a specifical... Read More about A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling.

Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples (2020)
Presentation / Conference Contribution
Babaagba, K., Tan, Z., & Hart, E. (2020, July). Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples. Presented at The 2020 IEEE Congress on Evolutionary Computation (IEEE CEC 2020), Glasgow, UK

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, we explore whether machine-learning models can be improved by augmenting t... Read More about Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples.

Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites (2020)
Presentation / Conference Contribution
Babaagba, K. O., Tan, Z., & Hart, E. (2020, April). Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites. Presented at EvoStar 2020, Seville, Spain

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 resistant to future attacks. In this paper, we use a Multi-dimensional Archi... Read More about Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites.

Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme (2019)
Presentation / Conference Contribution
Babaagba, K. O., Tan, Z., & Hart, E. (2019, November). Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme. Presented at The 5th International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications (DependSys 2019), Guangzhou, China

The ability to detect metamorphic malware has generated significant research interest over recent years, particularly given its proliferation on mobile devices. Such malware is particularly hard to detect via signature-based intrusion detection syste... Read More about Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme.

A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning (2019)
Presentation / Conference Contribution
Babaagba, K. O., & Adesanya, S. O. (2019, March). A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning. Presented at ICEIT 2019: 2019 8th International Conference on Educational and Information Technology, Cambridge, UK

In this paper, the effect of feature selection in malware detection using machine learning techniques is studied. We employ supervised and unsupervised machine learning algorithms with and without feature selection. These include both classification... Read More about A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning.