Kehinde Babaagba

kehinde babaagba

Dr Kehinde Babaagba

Lecturer

Biography

Kehinde Oluwatoyin Babaagba is a Lecturer at the School of Computing at Edinburgh Napier University. She gained a First Class BSC (Hons) in Computer Science from Redeemers University, Nigeria, where she also emerged as the best graduating student of her set. She also holds an MSC in Computing Information Engineering (Distinction) from Robert Gordon University, where she also emerged as class prize winner for the MSC in Computing Information Engineering programme. She holds a PhD in Computing from Edinburgh Napier University. She worked as Associate Lecturer during her PhD and University Tutor at Edinburgh Napier University. Prior to joining Napier, she worked as Assistant Lecturer at Redeemer’s University and Academic Trainee at Osun State University, Nigeria.

She has made contributions to the research community as well as industries within the context of applying AI techniques to solve real-world problems. During her masters, she worked with the Institute for Innovation, Design & Sustainability of Robert Gordon University on using competing mutating agents as a tool for improving the performance of genetic algorithms. Furthermore, she also contributed to the design of a system for analyzing a corpus of malicious instances using machine-learning strategies. Her PhD research paved the way towards prediction of malware evolution, which contributes invaluable knowledge to building robust systems that defend self-mutating malware. These have made significant contributions to cybersecurity specialists and researchers in securing the UK’s networked and online activities from misuse, as well as promote better wide-spread adoption of trusted and secure Artificial Intelligence (AI) systems across the UK’s digital economy. She has been able to publish some academic papers (https://scholar.google.com/citations?view_op=list_works&hl=en&user=5WYIQUsAAAAJ). Some of which have led to awards like Outstanding Student Contribution, Best Student Paper nomination and Excellent Oral Presentation.

More recently, Kehinde’s research interests include transfer learning, computer vision, generative adversarial networks as well as other machine learning applications in cybersecurity. She is also involved in teaching and supervision of various honors and masters’ projects. Besides, Kehinde is very passionate about mentoring young students interested in software engineering, data science and cybersecurity, to this end She is a STEM ambassador. This opportunity affords her the privilege of impacting knowledge and encouraging young students in their journey to becoming software engineers, data scientists and security specialists.

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Esteem
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Reviewing

• Finance Chair for the IEEE Conference on Dependable and Secure Computing (IEEE DSC 2022).
• Publication Chair for EvoAPPS 2022 part of Evo* which is the leading conference in Europe for Evolutionary Computation.
• Web and System Management co-chair for the IEEE International Conference on Smart City and Informatization (IEEE iSCI-2021).
• Reviewer for Journals like IEEE Transactions on Industry Informatics, Journal of Cybersecurity Technology.
• Participant of Student as Colleagues (SaC) in the review of teaching practices program with Edinburgh Napier University in partnership with the Department of Learning and Teaching Enhancement, from November 2018 to May 2019.
• Workshop Coordinator at the Scotland-wide SICSA (Scottish Informatics and Computer Science Alliance) 2018 PhD Conference.

Projects

• Evolutionary based Generative Adversarial Learning Approach to Metamorphic Malware Detection - SICSA Funded.
• Participant of the Edinburgh Napier University Data Driven Innovation City Deal Project.
• Member of the Curriculum Developers’ Network of Edinburgh Napier University's Data Driven Innovation City Deal project.

Professional Membership

• Member of IEEE including Women in Engineering society and Computational Intelligence society.
• Member of ACM.
• Associate Member of ALT.
• STEM Ambassador under the Scottish STEM Ambassador Hub.

Esteem

Advisory panels and expert committees or witness

  • Society for the Promotion of Evolutionary Computation in Europe and its Surroundings (SPECIES) - Board Member

 

Conference Organising Activity

  • Finance Chair for the IEEE Conference on Dependable and Secure Computing (IEEE DSC 2022).
  • Publication Chair for EvoAPPS 2022 part of Evo* which is the leading conference in Europe for Evolutionary Computation.
  • Web and System Management co-chair for the IEEE International Conference on Smart City and Informatization (IEEE iSCI-2021)
  • Workshop Coordinator at the Scotland-wide SICSA (Scottish Informatics and Computer Science Alliance) 2018 PhD Conference.

 

Fellowships and Awards

  • Associate Fellow of Advance HE
  • Best Paper (3rd place) for the IEEE Conference on Dependable and Secure Computing (IEEE DSC 2022)
  • Outstanding Contribution Award - 2020 Evo* (EuroGP-EvoApplications-EvoCOP-EvoMUSART 2020 conferences, known collectively as EvoStar)
  • Excellent Oral Presentation Award - 2019 ACM International Conference on Educational and Information Technology (ICEIT 2019)

 

Membership of Professional Body

  • Associate Member of Association for Learning Technology
  • STEM Ambassador under the Scottish STEM Ambassador Hub
  • Member of ACM
  • Member of IEEE

 

Public/Community Engagement

  • #DataYou by the Data Skills Gateway
  • Curriculum Developers’ Network of Edinburgh Napier University's Data Driven Innovation City Deal project

 

Reviewing

  • IEEE Transactions on Industry Informatics
  • Security and Communication Networks
  • Journal of Cybersecurity Technology
  • Student as Colleagues (SaC) in the review of teaching practices program with Edinburgh Napier University in partnership with the Department of Learning and Teaching Enhancement

 

Date


9 results

A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs

Conference Proceeding
McLaren, R. A., Babaagba, K., & Tan, Z. (in press)
A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs. In The 8th International Conference on machine Learning, Optimization and Data science - LOD 2022
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 a...

A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling

Conference Proceeding
Turnbull, L., Tan, Z., & Babaagba, K. (in press)
A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling. In The 2022 5th IEEE Conference on Dependable and Secure Computing (IEEE DSC 2022)
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 we...

Applications of Evolutionary Computation: 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings

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. Cham: 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...

Toward Machine Intelligence that Learns to Fingerprint Polymorphic Worms in IoT

Journal Article
Wang, F., Yang, S., Wang, C., Li, Q., Babaagba, K., & Tan, Z. (in press)
Toward Machine Intelligence that Learns to Fingerprint Polymorphic Worms in IoT. International Journal of Intelligent Systems, 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 sha...

Application of evolutionary machine learning in metamorphic malware analysis and detection

Thesis
Babaagba, K. O. Application of evolutionary machine learning in metamorphic malware analysis and detection. (Thesis)
Edinburgh Napier University. Retrieved from 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 hid...

Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples

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,...

Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites

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 r...

Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme

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 i...

A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning

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 algori...

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