文章基本信息

标题：Recurrent Neural Network for Malware Detection
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作者：Mudzfirah Abdul Halim ; Azizi Abdullah ; Khairul Akram Zainol Ariffin 等
期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2019
卷号：11
期号：1
页码：46-63
出版社：International Center for Scientific Research and Studies
摘要：Recently, an active development of network communication technology has brought inspiration to new cyber-attack such as malware. This possesses a massive threat to network organization, users and security. Consequently, many researchers have developed novel algorithms for attack detection. Nevertheless, they still face the problem of building reliable and accurate models that are capable in handling large quantities of data with changing patterns. The most common technique to represent the feature of malware is bag-of-words (BOW) where the frequency of each word is used for malware description. However, using BOW approach will destroy the spatial and sequence information aspects of malware patterns, resulting in information loss and coarse indexing. Therefore, this paper presents two combination models of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) to deal with spatial and temporal signals problem of BOW representation. Both techniques are well known in the classification problem with LSTM being useful in temporal modeling while CNN is good at extract spatial information from data. After that, the Multi-Layer Perceptron (MLP) is used for classification. The model is trained on Drebin dataset and validated, and then the result is compared with other techniques. The experiment shows that the both proposed models outperform common MLP, CNN and LSTM models on a malware classification task. Our best model (LSTM-CNN) model obtains state-of-the-art performance level of 98.53% of the Drebin dataset.
关键词：Deep Learning; Long Short Term Memory; Malware Classification; Recurrent Neural Network.