为虚拟机提供适应性和可扩展性保护框架,利用深度学习和动态防御
D Kanthasamy1, C N S Vinoth Kumar2
1Department of Networking and Communications, School of Computing, College of Engineering and Technology (CET), SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India.
Scientific reports
|November 26, 2025
概括
在虚拟机 (VM) 中,VMShield 提供了强大的防范网络威胁. 这种自适应系统通过准确检测和减轻低开销的复杂攻击来提高云安全性.
科学领域:
- 云计算安全 云计算安全
- 网络安全 网络安全
- 机器学习应用 机器学习应用
背景情况:
- 虚拟机 (VM) 提供灵活性,但对隐蔽的网络威胁很脆弱.
- 传统的防御与适应性攻击作斗争,导致高假阳性.
- 新兴的威胁,如资源枯竭和横向移动,挑战了当前的安全措施.
研究的目的:
- 为云托管的虚拟机 (VM) 开发一个灵活和适应性的防御系统.
- 减轻传统的基于签名和启发式的安全方法的局限性.
- 提供及时的威胁识别和迅速的缓解,低的开销.
主要方法:
- 实施了一条结合特征提取,异常检测,分类和缓解的管道.
- 使用自适应特征编码器来表示行为.
- 员工密度意识集群用于异常检测和变压器提升分类器用于威胁识别.
- 集成了一个动态缓解控制器用于运行时决策.
主要成果:
- 在基准数据集 (ToN-IoT,CSE-CIC-IDS2018) 上实现了99.8%的准确性,99.7%的精度和99.6%的F1分数.
- 与最先进的方法相比,虚假阳性减少了35%.
- 证明了可扩展性,检测延迟约为240毫秒,开销低于7%.
结论:
- 拟议的VMShield框架提供了对云虚拟机中新出现的对抗性威胁的准确和可扩展的防御.
- 该系统将准确性与运营弹性相结合,以实现实际的云安全.
- 这种适应性和可扩展的保护框架解决了动态虚拟机环境中传统防御的局限性.
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