Yousefnezhad Narges, Malhi Avleen, Främling Kary
Department of Computer Science, Aalto University, Tietotekniikantalo, Konemiehentie 2, 02150 Espoo, Finland.
Department of Computing and Informatics, Bournemouth University, Fern Barrow, Poole, Dorest BH12 5BB, UK.
Sensors (Basel). 2021 Apr 10;21(8):2660. doi: 10.3390/s21082660.
In an Internet of Things (IoT) environment, a large volume of potentially confidential data might be leaked from sensors installed everywhere. To ensure the authenticity of such sensitive data, it is important to initially verify the source of data and its identity. Practically, IoT device identification is the primary step toward a secure IoT system. An appropriate device identification approach can counteract malicious activities such as sending false data that trigger irreparable security issues in vital or emergency situations. Recent research indicates that primary identity metrics such as Internet Protocol (IP) or Media Access Control (MAC) addresses are insufficient due to their instability or easy accessibility. Thus, to identify an IoT device, analysis of the header information of packets by the sensors is of imperative consideration. This paper proposes a combination of sensor measurement and statistical feature sets in addition to a header feature set using a classification-based device identification framework. Various machine Learning algorithms have been adopted to identify different combinations of these feature sets to provide enhanced security in IoT devices. The proposed method has been evaluated through normal and under-attack circumstances by collecting real-time data from IoT devices connected in a lab setting to show the system robustness.
在物联网（IoT）环境中，大量潜在的机密数据可能会从安装在各处的传感器中泄露。为确保此类敏感数据的真实性，首先验证数据来源及其身份非常重要。实际上，物联网设备识别是迈向安全物联网系统的首要步骤。一种合适的设备识别方法可以抵制恶意活动，例如在关键或紧急情况下发送会引发无法弥补的安全问题的虚假数据。最近的研究表明，诸如互联网协议（IP）或媒体访问控制（MAC）地址等主要身份指标由于其不稳定性或易于获取性而不够充分。因此，为了识别物联网设备，传感器对数据包头部信息的分析是必须要考虑的。本文除了使用基于分类的设备识别框架的头部特征集之外，还提出了传感器测量和统计特征集的组合。已经采用了各种机器学习算法来识别这些特征集的不同组合，以增强物联网设备的安全性。通过在实验室环境中从连接的物联网设备收集实时数据，在正常和受攻击情况下对所提出的方法进行了评估，以展示系统的鲁棒性。
