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.

ImageBased Unknown Malware Classification with FewShot Learning Models Knowing malware types in every malware attacks is very helpful to the administrators to have proper defense policies for their system. It must be a massive benefit for the organization as well as the social if the automatic protection systems could themselves detect, classify an existence of new malware types in the whole network system with a few malware samples. This feature helps to prevent the spreading of malware as soon as any damage is caused to the networks. An approach introduced in this paper takes advantage of Oneshotfewshot learning algorithms in solving the malware classification problems by using some wellknown models such as Matching Networks, Prototypical Networks. To demonstrate an efficiency of the approach, we run the experiments on the two malware datasets (namely, MalImg and Microsoft Malware Classification Challenge), and both experiments all give us very high accuracies. We confirm that if applying models correctly from the machine learning area could bring excellent performance compared to the other traditional methods, open a new area of malware research.