Evolutionary based Transfer Learning Approach to Improving Classification of Metamorphic Malware

Babaagba, Kehinde O.; Ayodele, Mayowa

Abstract

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 quickly they obfuscate their code. It has also been shown that Machine learning (ML) models are not robust to these attacks due to the insufficient data to train these models resulting from the constant code mutation of metamorphic malware. Although recent studies have shown how to generate samples of metamorphic malware to serve as training data, this process can be computationally expensive. One way to improve the performance of these ML models is to transfer learning from other fields which have robust models such as what has been done with the transfer of learning from computer vision and image processing to improve malware detection. In this work, we introduce an evolutionary-based transfer learning approach that uses evolved mutants of malware generated using a traditional Evolutionary Algorithm (EA) as well as models from Natural Language Processing (NLP) text classification to improve the classification of metamorphic malware. Our preliminary results demonstrate that using NLP models can improve the classification of metamorphic malware in some instances.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: EvoApplications 2023: 26th International Conference on the Applications of Evolutionary Computation
• Start Date: Apr 12, 2023
• End Date: Apr 14, 2023
• Acceptance Date: Jan 18, 2023
• Online Publication Date: Apr 9, 2023
• Publication Date: Apr 10, 2023
• Deposit Date: Feb 15, 2023
• Publicly Available Date: Apr 10, 2024
• Publisher: Springer
• Pages: 161-176
• Series Title: Lecture Notes in Computer Science
• Series Number: 13989
• Series ISSN: 0302-9743
• Book Title: Applications of Evolutionary Computation – 26th International Conference, EvoApplications 2023
• ISBN: 9783031302282
• DOI: https://doi.org/10.1007/978-3-031-30229-9_11
• Related Public URLs: https://link.springer.com/book/9783031302282