Visible to the public An Effective Ensemble Deep Learning Framework for Malware Detection

TitleAn Effective Ensemble Deep Learning Framework for Malware Detection
Publication TypeConference Paper
Year of Publication2018
AuthorsSang, Dinh Viet, Cuong, Dang Manh, Cuong, Le Tran Bao
Conference NameProceedings of the Ninth International Symposium on Information and Communication Technology
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-6539-0
Keywordscomposability, Ensemble Method, malware detection, Metrics, pubcrawl, ransomware, Residual Convolutional Neural Network, Resiliency
AbstractMalware (or malicious software) is any program or file that brings harm to a computer system. Malware includes computer viruses, worms, trojan horses, rootkit, adware, ransomware and spyware. Due to the explosive growth in number and variety of malware, the demand of improving automatic malware detection has increased. Machine learning approaches are a natural choice to deal with this problem since they can automatically discover hidden patterns in large-scale datasets to distinguish malware from benign. In this paper, we propose different deep neural network architectures from simple to advanced ones. We then fuse hand-crafted and deep features, and combine all models together to make an overall effective ensemble framework for malware detection. The experiment results demonstrate the efficiency of our proposed method, which is capable to detect malware with accuracy of 96.24% on our large real-life dataset.
Citation Keysang_effective_2018