detection: code for deep android malware detection paper

Posted by fierce at 2020-02-27

This repository contains the code for the paper "Deep Android Malware Detection" (pdf download) | (citation) We use a convolutional neural network (CNN) for android malware classification. Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled android apk. Features indicative of malware are automatically learned from the raw opcode sequence thus removing the need for hand-engineered malware features. The network runs on GPU, allowing a very large number of files to be quickly scanned. If you use this code please cite the following paper:

@inproceedings{mclaughlin2017codaspy, title = "Deep Android Malware Detection", author = "Niall McLaughlin and {Martinez del Rincon}, Jesus and BooJoong Kang and Suleiman Yerima and Paul Miller and Sakir Sezer and Yeganeh Safaeisemnani and Erik Trickel and Ziming Zhao and Adam Doupé and {Joon Ahn}, Gail", year = "2016", month = "12", booktitle = "Proceeding of the ACM Conference on Data and Applications Security and Privacy (CODASPY) 2017", publisher = "Association for Computing Machinery (ACM)", }

How to run the code Given an existing dataset directory (see below for details), the file will do the following: Partition the dataset into training-set and held-out test-set Train a neural network Test the trained network on the test-set Prerequisites Dataset structure An example dataset with the required directory structure is provided in ./dataset The neural network requires opcode sequence files in the correct format, and a dataset directory with sub-directories containing malware and benign opcode sequence files. An example dataset directory is provided in ./dataset. The dataset directory must have the following structure: There must be a directory called 'Benign', and contains non-malware opcode sequences files The other directory can have any name ,and contains malware opcode sequence files Opcode Sequence files Opcode sequence files can be created from android APK files using the opcode sequence creation tool. This tool is located in ./opcodeseq_creator Please see the readme file in this directory for more information. Setup The neural network code is implemented using Torch. It is recommended to use a GPU to achieve acceleration of testing and training. For details on installing Torch please see The opcode sequence creator tool requires APKTool