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

Turnbull, Leigh; Tan, Zhiyuan; Babaagba, Kehinde

Authors



Abstract

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 specifically dangerous group of malicious software that perturbs its structure between generations. Detecting these types of malware, thus, appear to be more challenging. Recent research demonstrates that Machine Learning (ML) techniques outperform traditional methods in detecting known and uncategorised malware variants. Hence, this research aims to investigate the use of ML, a Generative Neural Network specifically, for enhancing metamorphic malware detection in Android (the most popular mobile operating system) via augmenting training data. The results show the augmented training data, containing novel samples derived from Deep Convolutional Generative Adversarial Network (DCGAN) and features from metamorphic malware samples, improves the detection performance of unseen metamorphic malware.

Presentation Conference Type Conference Paper (Published)
Conference Name The 2022 5th IEEE Conference on Dependable and Secure Computing (IEEE DSC 2022)
Start Date Jun 22, 2022
End Date Jun 24, 2022
Acceptance Date May 3, 2022
Online Publication Date Sep 26, 2022
Publication Date 2022
Deposit Date May 25, 2022
Publisher Institute of Electrical and Electronics Engineers
Book Title 2022 IEEE Conference on Dependable and Secure Computing (DSC)
DOI https://doi.org/10.1109/DSC54232.2022.9888906
Keywords Malicious Software, Metamorphic Malware, Machine Learning, Neural Network, Behaviour Profiling.
Public URL http://researchrepository.napier.ac.uk/Output/2874008