Data Augmentation with Generative Models for Improved Malware Detection: A Comparative Study* Generative Models have been very accommodating when it comes to generating artificial data. Two of the most popular and promising models are the Generative Adversarial Network (GAN) and Variational Autoencoder (VAE) models. They both play critical roles in classification problems by generating synthetic data to train classifier more accurately. Malware detection is the process of determining whether or not software is malicious on the hostu0027s system and diagnosing what type of attack it is. Without adequate amount of training data, it makes malware detection less efficient. In this paper, we compare the two generative models to generate synthetic training data to boost the Residual Network (ResNet18) classifier for malware detection. Experiment results show that adding synthetic malware samples generated by VAE to the training data improved the accuracy of ResNet18 by 2% as it compared to 6% by GAN.

MANNWARE: A Malware Classification Approach with a Few Samples Using a Memory Augmented Neural Network The ability to stop malware as soon as they start spreading will always play an important role in defending computer systems. It must be a huge benefit for organizations as well as society if intelligent defense systems could themselves detect and prevent new types of malware as soon as they reveal only a tiny amount of samples. An approach introduced in this paper takes advantage of OneshotFewshot learning algorithms to solve the malware classification problems using a Memory Augmented Neural Network in combination with the Natural Language Processing techniques such as word2vec, ngram. We embed the malwarexe2x80x99s API calls, which are very valuable sources of information for identifying malwarexe2x80x99s behaviors, in the different feature spaces, and then feed them to the oneshotfewshot learning models. Evaluating the model on the two datasets (FFRI 2017 and APIMDS) shows that the models with different parameters could yield high accuracy on malware classification with only a few samples. For example, on the APIMDS dataset, it was able to guess 78.85% correctly after seeing only nine malware samples and 89.59% after finetuning with a few other samples. The results confirmed very good accuracies compared to the other traditional methods, and point to a new area of malware research.