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dqy
44c6086b8c Merge branch 'main' into feature/cn-gpt
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2024-05-18 20:58:38 +08:00
dqy
27ec14be54 Merge pull request 'doc/add_banner' (#18) from doc/add_banner into main
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Reviewed-by: ccyj <ccyj@noreply.localhost>
Reviewed-by: dqy <dqy@noreply.localhost>
2024-05-18 20:54:42 +08:00
dqy
21d1a6f3cc Merge branch 'main' into doc/add_banner
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2024-05-18 20:54:00 +08:00
dqy
dbdfc1897c Merge pull request 'doc:增加检测Python对其他语言的封装' (#17) from doc/python-binding into main
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Reviewed-by: sangge <sangge@noreply.localhost>
Reviewed-by: dqy <dqy@noreply.localhost>
2024-05-18 20:51:51 +08:00
594e0934e5 Merge branch 'main' into doc/python-binding
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2024-05-14 19:54:49 +08:00
3e0dd66d31 doc: add project's banner
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2024-05-14 17:50:16 +08:00
24206b13af feat: add git lfs 2024-05-14 17:49:28 +08:00
3c7e5f4c3d doc:增加检测Python对其他语言的封装
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2024-05-11 21:37:35 +08:00
4bbe8ea62f doc:增加检测Python对其他语言的封装
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2024-05-05 20:12:43 +08:00
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# BackDoorBuster
![BackDoorBuster Banner](./banner.webp)
## 项目背景
随着网络安全威胁的增加,恶意软件和后门的检测成为了保护个人和组织数据安全的重要任务。后门通常被隐藏在合法软件中,给黑客提供远程控制目标系统的能力。本项目旨在开发一个工具,能够有效识别和评估潜在的后门风险。

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参考项目: [https://github.com/SonarSource/sonarqube]
检查源代码的语法和关键词。通过这种方式,可以发现是否存在与其他语言的交互,比如调用外部命令、使用其他语言的扩展模块、与其他语言的接口交互等。
实现方法可以使用Python代码解析库如ast模块来分析语法树并检查特定的代码模式或结构开发脚本来搜索Python代码中常用于与其他语言交互的关键词和函数例如ctypes、subprocess、os.system等
## 控制流分析
通过分析程序的控制流(即程序中各个操作的执行顺序),可以检测到异常的控制流路径,这些路径可能是后门的迹象。
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这个网站可以搜索依赖中是否存在漏洞: [https://security.snyk.io/package/pip/]
分析代码库中的依赖关系,查找是否导入了与其他语言交互相关的模块或库
实施策略:开发脚本进行依赖库对比匹配
## 异常行为检测
通过定义“正常”代码行为的基线,可以标识出异常行为,这些异常行为可能指示着后门的存在。
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使用NLP技术来训练机器学习模型以自动从大量代码中学习和识别异常或潜在的后门模式。
开发方法采用深度学习框架如TensorFlow或PyTorch结合NLP处理工具训练模型识别代码中的异常行为。