Al-Khwarizmi Institute of Computer Science (KICS), University of Engineering & Technology (UET), Lahore 54890, Pakistan.
Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal.
Sensors (Basel). 2021 Apr 26;21(9):3025. doi: 10.3390/s21093025.
The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices' security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.
物联网（IoT）作为一个备受关注的话题，在研究和工业界引起了广泛的关注，因为它对人类生活产生了革命性的影响。IoT 技术的快速发展通过引入智能设备、智能医疗、智能工业、智能城市、智能电网等概念，彻底改变了人类的生活。如今，物联网设备的安全性已成为一个严重的问题，特别是在医疗保健领域，最近的攻击暴露了物联网安全漏洞的破坏性。传统的网络安全解决方案已经成熟。然而，由于 IoT 设备的资源约束特性和 IoT 协议的独特行为，现有的安全机制不能直接部署用于保护 IoT 设备和网络免受网络攻击。为了提高物联网的安全性，研究人员需要物联网特定的工具、方法和数据集。为了解决上述问题，我们提供了一个开发物联网上下文感知安全解决方案的框架，以检测物联网用例中的恶意流量。所提出的框架由一个新创建的、开源的物联网数据生成工具 IoT-Flock 组成。IoT-Flock 工具允许研究人员开发由正常和恶意 IoT 设备组成的物联网用例并生成流量。此外，所提出的框架提供了一个开源实用程序，用于将 IoT-Flock 生成的捕获流量转换为物联网数据集。在这项研究中，我们首先生成了一个包含正常和物联网攻击流量的物联网医疗保健数据集。然后，我们将不同的机器学习技术应用于生成的数据集，以检测网络攻击并保护医疗保健系统免受网络攻击。所提出的框架将有助于开发上下文感知的物联网安全解决方案，特别是对于物联网医疗保健环境等敏感用例。
