物联网恶意软件:基于属性的分类,检测机制和挑战
Princy Victor1, Arash Habibi Lashkari2, Rongxing Lu1
1Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3B 5A3 Canada.
概括
本研究介绍了物联网 (IoT) 恶意软件的综合分类,详细介绍了各种类别的100个属性. 它分析了77个已识别的物联网恶意软件,并审查了当前的检测方法,以指导未来的研究.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 网络安全 网络安全
背景情况:
- 物联网 (IoT) 迅速使社会数字化,改善了供应链,但也创造了新的漏洞.
- 物联网设备的多样性使它们成为恶意软件作者有吸引力的目标.
- 目前的研究往往缺乏对物联网恶意软件多面性质的深入理解.
研究的目的:
- 通过创建详细的分类学来建立对物联网恶意软件的基本理解.
- 分类和分析物联网恶意软件的各个方面,包括攻击载体,特征和检测方法.
- 提供对挑战的洞察力,并指导物联网恶意软件检测和缓解的未来研究.
主要方法:
- 开发一个物联网恶意软件分类系统,包括100个属性.
- 基于攻击类型,表面,分布,受害者设备,特征,访问机制,编程语言和协议的分类.
- 将分类系统映射到77个已识别的物联网恶意软件 (2008-2022) 和审查现有的检测工作.
主要成果:
- 一个结构化的物联网恶意软件分类系统,具有100个不同的属性.
- 77个现实世界物联网恶意软件实例的分析和映射.
- 确定当前物联网恶意软件检测研究中的关键领域和挑战.
结论:
- 对物联网恶意软件的全面了解对于有效的安全增强至关重要.
- 拟议的分类学为分析和比较物联网恶意软件提供了一个标准化的框架.
- 需要进一步的研究来解决物联网威胁的不断变化的局面,并提高检测效率.
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