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Security and Fault Detection in In-node components of IIoT Constrained Devices

Abstract:

The last decade has witnessed a paradigm change in Industrial Control Systems (ICS), from closed, isolated systems to fully-connected, Internet-capable systems. At the same time, new wireless technologies, mainly coming from the Internet of Things domain, emerged. IEEE 802.15.4 wireless-based standards like WirelessHART, ISA100.11a, ZigBee, and WIA-PA are increasingly used for monitoring industrial processes. Nevertheless, along with this paradigm change, some new threats appeared that menace current industrial infrastructures and economies (e.g., Stuxnet, Mariposa, Slammer). Additionally, in order to keep up with the requirements of new Industry 4.0 applications, sensor nodes software and hardware are becoming more complex and, thus, more prone to faults. In this paper, using a monitoring architecture proposed in our previous work, we injected and subsequently proceeded to detect representative firmware and hardware anomalies (namely, buffer overflow attacks, SPI faults, under-voltage, and high temperature faults) that can be used by attackers to cause major losses or even damage industrial control systems. We evaluated the performance of several machine learning techniques commonly used to detect anomalies (i.e. OCSVM, kNN, AutoEnconder), in order to determine if they could be useful to detect such faults. The obtained results demonstrate that simple and broad scope classifiers, using features that consume little resources, can be developed to detect such faults.