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基于Opcode2vec和CVAE-GANAN的工业控制系统中的恶意软件识别方法

Yuchen Huang1, Jingwen Liu1, Xuanyi Xiang2

  • 1The School of Computer Science, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)
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概括
此摘要是机器生成的。

本研究引入了一种新的方法,以改善工业控制系统 (ICS) 中的恶意软件检测. 该方法增强了机器学习分类器,大大提高了ICS网络安全的准确性和稳定性.

关键词:
网络安全 网络安全工业控制系统 工业控制系统机器学习是机器学习.恶意软件识别 恶意软件识别

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科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 工业控制系统 工业控制系统

背景情况:

  • 由于互联网的整合,工业控制系统 (ICS) 越来越容易受到恶意软件的攻击.
  • 现有的机器学习 (ML) 恶意软件识别方法在ICS环境中表现不佳.

研究的目的:

  • 提出一种基于机器学习的创新方法,用于增强恶意软件识别,专门为ICS环境量身定制.
  • 提高ICS中的恶意软件分类器的稳定性和稳定性.

主要方法:

  • 集成opcode2vec与预处理的功能.
  • 使用一个有条件变化的自编码器生成对抗网络 (CVAE-GAN).
  • 使用卷积神经网络 (CNN) 来进行恶意软件分类.

主要成果:

  • 实现了高性能指标:97.30%的准确性,92.34%的精度,97.44%的回忆力,94.82%的F1分数.
  • 在ICS中证明了恶意软件分类器的稳定性和强度的提高.
  • 通过广泛的实验,验证了拟议方法的有效性.

结论:

  • 拟议的方法显著提高了ICS中的恶意软件识别性能.
  • 量身定制的方法为ICS网络安全挑战提供了强大的解决方案.
  • 取得的结果代表了实验环境中报告的最高值.