Online Recursive Detection and Adaptive Fuzzy Mitigation of Cyber-Physical Attacks Targeting Topology of IMG: An LFC Case Study

Due to the low inertia of inverter-based islanded microgrids (IMGs), these systems require a delicate and accurate load frequency control (LFC) scheme. The deployment of such a control scheme, which preserves the balance between the load and generation, needs a cyber layer on top of the physical system that makes IMGs an appealing target for a variety of cyber-physical attacks (CPAs). Among these CPAs, there is a family of malicious CPAs whose aim is to compromise the LFC scheme by changing the topology of IMG and its parameters. On this basis, an online system identification method is developed to estimate the parameters of IMG using the recursive least square forgetting factor (RLS-FF) approach. Then, based on the estimated parameters, an anomaly-based intrusion detection system (IDS) is developed to identify CPAs and distinguish them from the uncertainties in the normal operation of IMG. Following anomaly detection, a mitigation scheme is proposed to regulate the IMG’s frequency using an adaptive interval type-2 fuzzy logic controller (IT2FLC). The proposed IT2FLC uses different types of distributed energy resources (DERs)—i.e., tidal power plants and solar panels which are, respectively, equipped with inertia emulation and droop-based controllers—to improve the frequency excursion resulting from CPAs. The simulation results verify the performance of the developed detection and mitigation schemes, particularly when the RLS-FF parameters, i.e., forgetting factor, covariance matrix, and reset parameter, are obtained through the grey wolf optimization (GWO) algorithm. Furthermore, the designed mitigation scheme is corroborated by comparing its performance with several well-known attack-resilient control frameworks in LFC studies, e.g., linear quadratic regulator (LQR) and H∞, using real-time simulations.©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

A self-integration testbed for decentralized socio-technical systems

The Internet of Things (IoT) comes along with new challenges for experimenting, testing, and operating decentralized socio-technical systems at large-scale. In such systems, autonomous agents interact locally with their users, and remotely with other agents to make intelligent collective choices. Via these interactions they self-regulate the consumption and production of distributed (common) resources, e.g., self-management of traffic flows and power demand in Smart Cities. While such complex systems are often deployed and operated using centralized computing infrastructures, the socio-technical nature of these decentralized systems requires new value-sensitive design paradigms; empowering trust, transparency, and alignment with citizens’ social values, such as privacy preservation, autonomy, and fairness among citizens’ choices. Currently, instruments and tools to study such systems and guide the prototyping process from simulation, to live deployment, and ultimately to a robust operation of a high Technology Readiness Level (TRL) are missing, or not practical in this distributed socio-technical context. This paper bridges this gap by introducing a novel testbed architecture for decentralized socio-technical systems running on IoT. This new architecture is designed for a seamless reusability of (i) application-independent decentralized services by an IoT application, and (ii) different IoT applications by the same decentralized service. This dual self-integration promises IoT applications that are simpler to prototype, and can interoperate with decentralized services during runtime to self-integrate more complex functionality, e.g., data analytics, distributed artificial intelligence. Additionally, such integration provides stronger validation of IoT applications, and improves resource utilization, as computational resources are shared, thus cutting down deployment and operational costs. Pressure and crash tests during continuous operations of several weeks, with more than 80K network joining and leaving of agents, 2.4M parameter changes, and 100M communicated messages, confirm the robustness and practicality of the testbed architecture. This work promises new pathways for managing the prototyping and deployment complexity of decentralized socio-technical systems running on IoT, whose complexity has so far hindered the adoption of value-sensitive self-management approaches in Smart Cities