Malware Detection Using Gist Features and Deep Neural Network

Malware is a virus file which causes damage to system files like executable files, documents, program files. This intent affects the performance of the system. Malware detection is vital with occurrence of malicious code on the internet and it provides an early warning for the computer security regarding malware and cyber-attacks. Real time malware detection is still a challenge though there is a considerable research showing advances in methods that can automatically predict the malicious of a specific file, program. Though the existing malware scanner can recognize the infected file, it produces the conflicting decisions and the accuracy of prediction is still not promising. Hence it is proposed to develop an accurate malware identification model using intelligent learning method. In this paper, malware detection problem is formulated as binary classification task and appropriate solution is obtained using machine learning. A database consisting of 400 executable files of which 200 virus samples and 200 benign samples have been used to prepare the training dataset. All the executable files have been converted into gray scale images from which the GIST features are derived. The contemporary deep learning is adopted to build the binary classifier which takes the GIST features as input. The experimental results provide an accuracy of over 81% in discriminating malware and benign files. It is reported that deep neural network based binary classification achieved improved predictive performance when compared with supervised learning.