All Outputs (22)

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.

Multi-Objective Evolutionary Algorithm for Automatic Generation of Adversarial Metamorphic Malware (2024)
Presentation / Conference Contribution
Babaagba, K., 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 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.

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.-K. Raymond Choo, & S. Hakak (Eds.), Internet of Things Security and Privacy: Practical and Management Perspectives (24-53). CRC Press. https://doi.org/10.1201/9781003199410-2

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.

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.

Applications of Evolutionary Computation: 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings (2022)
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. 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, in April 2022, co-located with the Evo*2022 events EuroGP, EvoCOP, and Ev... Read More about Applications of Evolutionary Computation: 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings.

Toward Machine Intelligence that Learns to Fingerprint Polymorphic Worms in IoT (2022)
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 share of total Internet traffic. However, the security and privacy of IoT and... Read More about Toward Machine Intelligence that Learns to Fingerprint Polymorphic Worms in IoT.

Application of evolutionary machine learning in metamorphic malware analysis and detection (2021)
Thesis
Babaagba, K. O. Application of evolutionary machine learning in metamorphic malware analysis and detection. (Thesis). Edinburgh Napier University. 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 hiding malicious instructions within normal program code. Current malware dete... Read More about Application of evolutionary machine learning in metamorphic malware analysis and detection.

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.