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相关实验视频

Updated: Jul 8, 2025

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实例攻击:基于解释的漏洞分析框架,针对DNN进行恶意软件检测.

Ruijin Sun1, Shize Guo2, Changyou Xing1

  • 1Army Engineering University of PLA, Nanjing, China.

PeerJ. Computer science
|December 11, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于实例的攻击,用于深度神经网络恶意软件检测. 它有效地在黑盒设置中生成对抗性示例,提高了探测器的稳定性.

关键词:
对抗性的例子.DNN DNN 在线可以解释的可解释.恶意软件是一种恶意软件.

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科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 软件工程 软件工程 软件工程

背景情况:

  • 深度神经网络 (DNN) 对于恶意软件检测至关重要,但它们的稳定性令人担忧.
  • 为DNN生成对抗性示例通常需要模型知识或广泛的数据,这些数据通常是不可用的.
  • 现有的方法在现实世界黑子场景中面临局限性.

研究的目的:

  • 引入一种可解释的,基于实例的攻击方法,用于在黑子环境中检测DNN恶意软件.
  • 为数据分节开发一种新的功能维护转换算法.
  • 为了提高恶意软件检测系统的稳定性.

主要方法:

  • 使用特定的二进制和恶意软件分类器开发了一个基于实例的攻击.
  • 使用数据增强来训练一个简单的,可解释的模型.
  • 引入了一个功能保护的转换算法,针对数据子节.
  • 利用二进制多元化来中和有影响力的部分,以产生对抗性的例子.

主要成果:

  • 基于实例的攻击成功生成了对抗性示例,在某些情况下几乎100%成功地欺骗DNN.
  • 确定数据子部分对恶意软件检测有重大影响.
  • 与最先进的对抗性攻击方法相比,表现出优越的性能.
  • 在黑盒设置中验证了该技术的有效性.

结论:

  • 拟议的基于实例的攻击是有效的,并且可以用于DNN恶意软件检测.
  • 新的转换算法和二进制多样化增强了对抗性示例生成.
  • 这种方法为改善恶意软件检测器在现实环境中的稳定性提供了实际解决方案.