Recurrent neural network for detecting malware

Abstract

In this paper, we propose an efficient Recurrent Neural Network (RNN) to detect malware. RNN is a classification of artificial neural networks connected between nodes to form a directed graph alongside with a temporal sequence. In this paper, we have conducted several experiments using different values of hyper parameters. From our rigorous experimentations, we found that the step size is a more important factor than the input size when using RNN for malware classification. To justify the proof-of-concept for RNN as an efficient approach for malware detection, we measured the performance of RNN with three different feature vectors using hyper parameters. The three feature vectors are “hot encoding feature vector”, “random feature vector” and “Word2Vec feature vector”. We also performed a pairwise t-test to test the results if they are significant with each other. Our results show that, RNN with Word2Vec feature vector achieved the highest Area Under the Curve (AUC) value and a good variance among three feature vectors. From the empirical analysis, we conclude that RNN with feature vectors pertained by the Skip-gram architecture of Word2Vec model is best for malware detection with high performance and stability.