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Outputs (24)

Multi-Objective Evolutionary Algorithm for Automatic Generation of Adversarial Metamorphic Malware (2025)
Presentation / Conference Contribution
Babaagba, K. O., Wylie, J., Ayodele, M., & Tan, Z. (2024, September). Multi-Objective Evolutionary Algorithm for Automatic Generation of Adversarial Metamorphic Malware. Presented at 29th European Symposium on Research in Computer Security - SECAI, Bydgoszcz, Poland

The rise of metamorphic malware, a dangerous type of malware, has sparked growing research interest due to its increasing attacks on information assets and computer networks. Sophos’ recent threat report reveals that 94% of malware targeting organiza... Read More about Multi-Objective Evolutionary Algorithm for Automatic Generation of Adversarial Metamorphic Malware.

Image Encryption Using Dynamic Salt Injection, Hybrid Chaotic Maps, and Dual Operation Substitution (2024)
Presentation / Conference Contribution
Khattak, A. A., Babaagba, K., Liu, X., & Shah, S. A. (2024, August). Image Encryption Using Dynamic Salt Injection, Hybrid Chaotic Maps, and Dual Operation Substitution. Presented at The 2024 International Conference on Automation and Computing (ICAC 2024), Sunderland, UK

In today's digital world, securing the multimedia data, especially images transmitted over unsecure networks, is critically important. In this paper, a novel chaos-based image encryption algorithm is proposed, which utilises dynamic salt injection, h... Read More about Image Encryption Using Dynamic Salt Injection, Hybrid Chaotic Maps, and Dual Operation Substitution.

Graph Injection Attack based on Node Similarity and Non-linear Feature Injection Strategy (2024)
Presentation / Conference Contribution
Li, Q., Gao, Y., Wang, F., Wang, C., Babaagba, K. O., & Tan, Z. (2024, October). Graph Injection Attack based on Node Similarity and Non-linear Feature Injection Strategy. Presented at 20th EAI International Conference on Security and Privacy in Communication Networks, Dubai, United Arab Emirates

Graph Neural Networks (GNNs) exhibit promise in the domains of network analysis and recommendation systems. Notwithstanding , these networks are susceptible to node injection attacks. To mitigate this vulnerability, the academic community has put for... Read More about Graph Injection Attack based on Node Similarity and Non-linear Feature Injection Strategy.

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. (2023, August). Image Forgery Detection using Cryptography and Deep Learning. Presented at EAI BDTA 2023 - 13th EAI International Conference on Big Data Technologies and Applications, Edinburgh

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.