模糊内存恶意软件检测在资源有限的物联网设备的智能城市应用程序
Sakib Shahriar Shafin1,2, Gour Karmakar1,2, Iven Mareels2
1Centre for Smart Analytics (CSA), Federation University Australia, Ballarat, VIC 3350, Australia.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了一种轻量级的混合机器学习模型,用于检测模糊内存恶意软件 (OMM). 这种新的方法有效地识别了资源有限的物联网设备上的各种恶意软件类型,这对智能城市安全至关重要.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 嵌入式系统 嵌入式系统
背景情况:
- 模糊内存恶意软件 (OMM) 对智能城市等相互连接的系统构成重大威胁.
- 现有的OMM检测方法往往以二进制为重点,范围有限,不适合资源有限的物联网设备.
研究的目的:
- 为嵌入式设备提出一个多类,轻量级的恶意软件检测方法.
- 解决现有的OMM检测技术在范围和资源要求方面的局限性.
主要方法:
- 一个混合模型,将卷积神经网络 (CNN) 结合起来,用于特征学习和双向长短期记忆 (BiLSTM) 用于时间建模.
- 开发一个适合物联网部署的紧和快速处理架构.
主要成果:
- 与现有的机器学习模型相比,拟议的方法在检测OMM和识别特定攻击类型方面表现出卓越的性能.
- 使用CIC-Malmem-2022 OMM数据集的实验验证证证了该方法的有效性.
结论:
- 开发的混合模型提供了一个强大而紧的解决方案,用于防御物联网设备上的模糊恶意软件.
- 这种方法通过在嵌入式系统中实现高效的恶意软件检测来提高智能城市应用程序的安全性.
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