Benchmarking Convolutional and Recurrent Neural Networks for Malware Classification
Malware (Computer software), Neural networks (Computer science), Deep learning (Machine learning), Computer security
Computer Engineering | Computer Sciences | Electrical and Computer Engineering
Malware detection and classification are attracting more research nowadays due to the increasing number of malware and ransomware instances targeting financial, educational and industrial systems. In artificial intelligence, we witness a resurgence of neural networks against the symbolic school which is manifested by many breakthroughs in gaming, image recognition and natural language processing under the umbrella term of deep learning. Researchers are evaluating deep learning algorithms for static and behavioral malware analysis. In this paper, we benchmark deep learning architectures composed of recurrent and convolutional neural networks. We report results on different techniques such as Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), and one-dimensional Convolutional Neural Networks (1DCNN). We develop an automatic feature extraction component and a hybrid CNN/RNN classification model. We validate our model using the Microsoft Malware Classification Challenge (BIG 2015). Our results show comparable accuracy to approaches requiring manual and thorough feature engineering.
Safa, Haidar; Nassar, Mohamed; and Al Orabi, Wael Al Rahal, "Benchmarking Convolutional and Recurrent Neural Networks for Malware Classification" (2019). Electrical & Computer Engineering and Computer Science Faculty Publications. 119.
H. Safa, M. Nassar and W. A. Rahal Al Orabi, "Benchmarking Convolutional and Recurrent Neural Networks for Malware Classification," 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019, pp. 561-566, doi: 10.1109/IWCMC.2019.8766515.