عنوان انگلیسی مقاله:
A multi-level deep learning system for malware detection
ترجمه فارسی عنوان مقاله:
یک سیستم یادگیری عمیق چند سطحی برای تشخیص بدافزار
Sciencedirect - Elsevier - Expert Systems With Applications, 133 (2019) 151-162: doi:10:1016/j:eswa:2019:04:064
Wei Zhong a , ∗, Feng Gu b
To defend against an increasing number of sophisticated malware attacks, deep-learning based Malware Detection Systems (MDSs) have become a vital component of our economic and national security. Tra- ditionally, researchers build the single deep learning model using the entire dataset. However, the sin- gle deep learning model may not handle the increasingly complex malware data distributions effectively since different sam ple subspaces representing a group of similar malware may have unique data distribu- tion. In order to further improve the performance of deep learning based MDSs, we propose a Multi-Level Deep Learning System (MLDLS) that organizes multiple deep learning models using the tree structure. Each model in the tree structure of MLDLS was not built on the whole dataset. Instead, each deep learn- ing model focuses on learning a specific data distribution for a particular group of malware and all deep learning models in the tree work together to make a final decision. Consequently, the learning effective- ness of each deep learning model built for one cluster can be improved. Experimental results show that our proposed system performs better than the traditional approach.
Keywords: Malware detection | Deep learning | Multi-level clustering algorithm | Convolutional neural network | Recurrent neural network | Model construction time