- Title
- Generative malware outbreak detection
- Creator
- Park, Sean; Gondal, Iqbal; Kamruzzaman, Joarder; Oliver, Jon
- Date
- 2019
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/172026
- Identifier
- vital:14453
- Identifier
-
https://doi.org/10.1109/ICIT.2019.8754939
- Identifier
- ISBN:9781538663769 (ISBN)
- Abstract
- Recently several deep learning approaches have been attempted to detect malware binaries using convolutional neural networks and stacked deep autoencoders. Although they have shown respectable performance on a large corpus of dataset, practical defense systems require precise detection during the malware outbreaks where only a handful of samples are available. This paper demonstrates the effectiveness of the latent representations obtained through the adversarial autoencoder for malware outbreak detection. Using instruction sequence distribution mapped to a semantic latent vector, the model provides a highly effective neural signature that helps detecting variants of a previously identified malware within a campaign mutated with minor functional upgrade, function shuffling, or slightly modified obfuscations. The method demonstrates how adversarial autoencoder can turn a multiclass classification task into a clustering problem when the sample set size is limited and the distribution is biased. The model performance is evaluated on OS X malware dataset against traditional machine learning models. © 2019 IEEE.; E1
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 2019 IEEE International Conference on Industrial Technology, ICIT 2019 Vol. 2019-February, p. 1149-1154
- Rights
- Copyright ©2019 IEEE
- Rights
- 8754939
- Rights
- This metadata is freely available under a CCO license
- Subject
- Deep learning; Generative Adversarial Autoencoder; Malware outbreak detection; Obfuscation; Semantic Hashing
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