Generative Malware Outbreak Detection
Sean S. Park, Iqbal Gondal, Joarder Kamruzzaman, Jon Oliver · 2019
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.