Time Synchronization Attack Scenarios and Analysis of Effective SelfDetection Parameters in a Distributed Industrial Wireless Sensor Network Time Synchronization is an essential communication component for Industrial Wireless Sensor Networks (IWSNs). An IWSN is composed of multiple distributed resourceconstrained sensor nodes, which require their local clocks to be synchronized with each other in order to exchange packets in real time precisely ordered schedule. Time synchronization strategies are not designed with security support. As a consequence, a new generation of attacks arose, specifically targeting the corresponding protocols. Recent research studies prove that some attacks are practical and severe. At the same time, the set of detection parameters proposed in emerging attack detection techniques are not sufficient to catch up on the attacks. In this paper, we analyze in depth some attack strategies and develop new useful detection parameters. The key concept behind our mechanism is to use the MAC layer protocol characteristics to figure out novel metrics as inputs for a selfdetection of time synchronization attacks.

Autonomous Detection of Synchronization Attacks in the Industrial Internet Of Things Time Synchronization is an essential communication component for Industrial Wireless Sensor Networks (IWSNs). An IWSN is composed of multiple distributed resourceconstrained sensor nodes, which require their local clocks to be synchronized with each other to exchange packets in a precisely real time ordered schedule. Time synchronization protocols are not outlined with security support. As a consequence, a new generation of attacks arose, explicitly targeting the corresponding protocols. Recent research studies 1, 2, 3 prove that synchronization attacks are severe and practical. Without detection mechanisms, synchronization attacks bypass security defenses. A methodology for the detection of synchronization attacks based on characteristics of the synchronization protocol is proposed in this paper. Thus, we propose a novel method based on machine learning algorithms for detecting IIoT synchronization attacks. Applying such strategies for cybersecurity in IIoT requires the availability of substantial synchronization IIoT attack data. In our research, the reallife equivalent simulations using the open source OpenWSN simulator has been utilized for generation of high fidelity attack area, within the 6TiSCH industrial network. We believe that the IIoT attack dataset generated in this work can be used for further research and for detecting high layer attacks too. Experimentation was carried out using six learning algorithms to detect the attack and classify them from legitimate behavior.