All Outputs (12)

Image Forgery Detection using Cryptography and Deep Learning (2024)
Conference Proceeding
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)
Conference Proceeding
Tahir, A., Tan, Z., & Babaagba, K. O. (2024). Can Federated Models Be Rectified Through Learning Negative Gradients?. In Big Data Technologies and Applications (18-32). https://doi.org/10.1007/978-3-031-52265-9_2

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?.

Emotion Recognition on Social Media Using Natural Language Processing (NLP) Techniques (2023)
Conference Proceeding
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 Conference on Information Science and Systems (113-118). https://doi.org/10.1145/3625156.3625173

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.

Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices (2023)
Conference Proceeding
Spalding, A., Tan, Z., & Babaagba, K. O. (in press). Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices. In Proceedings of the 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom-2023)

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)
Conference Proceeding
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 (1753-1759). https://doi.org/10.1145/3583133.3596362

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)
Conference Proceeding
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://doi.org/10.1007/978-3-031-30229-9_11

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)
Conference Proceeding
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 Pontignano, Italy, September 19–22, 2022, Revised Selected Papers, Part II (32-46). https://doi.org/10.1007/978-3-031-25891-6_4

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)
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 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)
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, 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)
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 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)
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 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)
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 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.