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

Babaagba, Kehinde O.; Tan, Zhiyuan; Hart, Emma

Abstract

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 systems due to its ability to change its code over time. This article describes a novel framework which generates sets of potential mutants and then uses them as training data to inform the development of improved detection methods (either in two separate phases or in an adversarial learning setting). We outline a method to implement the mutant generation step using an evolutionary algorithm, providing preliminary results that show that the concept is viable as the first steps towards instantiation of the full framework.

Citation

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

• Presentation Conference Type: Conference Paper (published)
• Conference Name: The 5th International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications (DependSys 2019)
• Start Date: Nov 12, 2019
• End Date: Nov 15, 2019
• Acceptance Date: Aug 25, 2019
• Online Publication Date: Nov 5, 2019
• Publication Date: 2019
• Deposit Date: Sep 20, 2019
• Publicly Available Date: Nov 5, 2019
• Publisher: Springer
• Pages: 369-382
• Series Title: Communications in Computer and Information Science
• Series Number: 1123
• Series ISSN: 1865-0929
• Book Title: Dependability in Sensor, Cloud, and Big Data Systems and Applications
• ISBN: 9789811513039
• DOI: https://doi.org/10.1007/978-981-15-1304-6_29
• Keywords: Metamorphic Malware; Evolutionary Algorithm; Mutant Generation; Mobile Devices; Detection Methods; Adversarial Learning
• Public URL: http://researchrepository.napier.ac.uk/Output/2150256