Detection of Malicious Code Variants Based on a Flexible and Lightweight Net

With the phenomenon of code reuse is becoming more widespread in the same malicious family, this paper proposed a method to detect malicious code using a novel neural net. To implement our proposed detection method, malicious code was transformed into RGB images according to its binary sequence. Then, because of code reuse features can be revealed in the image, the images were identified and classified automatically using a flexible and lightweight neural net. In addition, we utilized dropout algorithm to address the data imbalance among different malware families. The experimental results demonstrated that our model performs well in accuracy and rate of convergence as compared with other models.