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)
|September 14, 2024
概括
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.
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.
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