Deep-Android-Malware-Detection - Code for Deep Android Malware Detection paper

项目说明

Deep Android Malware Detection

This repository contains the code for the paper "Deep Android Malware Detection" (pdf download) | (citation)

We use a con 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.

<p><img src='malware_network_diagram.png'></p>

If you use this code please cite the following paper:

How to run the code

Given an existing dataset directory (see below for details), the run.sh file will do the following:

1. Partition the dataset into training-set and held-out test-set
2. Train a neural network
3. 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:

1. There must be a directory called 'Benign', and contains non-malware opcode sequences files
2. 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 achie of testing and training. For details on installing Torch please see

The opcode sequence creator tool requires APKTool

项目信息

开发语言：Lua

Star 数：179

技术要点

该项目属于病毒分析方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕Code for Deep Android Malware Detection paper展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。