Smart grids security challenges: Classification by sources of threats

Smart metering systems generally referred to as the next-generation power measurement system, is considered as a revolutionary and evolutionary regime of existing power grids. More importantly, with integration of advanced computing and communication technologies, the smart meter (SM) is expected to greatly enhance efficiency and reliability of future power systems with renewable energy resources, as well as distributed intelligence and demand response. Different electrical energy metering standards are point of concern for power/energy measurements. As measurement standards are formed, systems built around them can become interoperable from a standards point of view but still have incompatible configurations or different maturity levels, or include non-standardized functions. Even in areas that are standardized, there are sometimes implementation decisions that can result in different measurement and security behavior. With this paper we make three contributions: firstly, we identify various 1-channel and 3-channel metrology integrated circuits (ICs), which are mandatory for the standard measurement of distributed and renewable electricity generation. Secondly, we describe harmonics effect on metrology, which impacts on reliability of widespread smart metering infrastructure. Finally, we develop and describe a comprehensive set of security issues for SMs. Specifically, we focus on reviewing and discussing smart metrology meter (SMM) applications (i.e. metrological functions and real-time monitoring functions), security requirements, network vulnerabilities, attack countermeasures, secure communication protocols required in smart grid (SG) architectures. This review will enable the researchers, public policy makers and stakeholders to open the mind to explore possible in an evolving energy domain as well as beyond this area.

Performance analysis of smart metering for smart grid: An overview

Cyber-physical systems (CPS) are interconnected architectures that employ analog and digital components as well as communication and computational resources for their operation and interaction with the physical environment. CPS constitute the backbone of enterprise (e.g., smart cities), industrial (e.g., smart manufacturing), and critical infrastructure (e.g., energy systems). Thus, their vital importance, interoperability, and plurality of computing devices make them prominent targets for malicious attacks aiming to disrupt their operations. Attacks targeting cyber-physical energy systems (CPES), given their mission-critical nature within the power grid infrastructure, can lead to disastrous consequences. The security of CPES can be enhanced by leveraging testbed capabilities in order to replicate and understand power systems operating conditions, discover vulnerabilities, develop security countermeasures, and evaluate grid operation under fault-induced or maliciously constructed scenarios. Adequately modeling and reproducing the behavior of CPS could be a challenging task. In this paper, we provide a comprehensive overview of the CPS security landscape with an emphasis on CPES. Specifically, we demonstrate a threat modeling methodology to accurately represent the CPS elements, their interdependencies, as well as the possible attack entry points and system vulnerabilities. Leveraging the threat model formulation, we present a CPS framework designed to delineate the hardware, software, and modeling resources required to simulate the CPS and construct high-fidelity models that can be used to evaluate the system’s performance under adverse scenarios. The system performance is assessed using scenario-specific metrics, while risk assessment enables the system vulnerability prioritization factoring the impact on the system operation. The overarching framework for modeling, simulating, assessing, and mitigating attacks in a CPS is illustrated using four representative attack scenarios targeting CPES. The key objective of this paper is to demonstrate a step-by-step process that can be used to enact in-depth cybersecurity analyses, thus leading to more resilient and secure CPS.

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Cyber-Physical Energy Systems Security: Threat Modeling, Risk Assessment, Resources, Metrics, and Case Studies

Anomaly detection has been used for decades to identify and extract anomalous components from data. Many techniques have been used to detect anomalies. One of the increasingly significant techniques is Machine Learning (ML), which plays an important role in this area. In this research paper, we conduct a Systematic Literature Review (SLR) which analyzes ML models that detect anomalies in their application. Our review analyzes the models from four perspectives; the applications of anomaly detection, ML techniques, performance metrics for ML models, and the classification of anomaly detection. In our review, we have identified 290 research articles, written from 2000-2020, that discuss ML techniques for anomaly detection. After analyzing the selected research articles, we present 43 different applications of anomaly detection found in the selected research articles. Moreover, we identify 29 distinct ML models used in the identification of anomalies. Finally, we present 22 different datasets that are applied in experiments on anomaly detection, as well as many other general datasets. In addition, we observe that unsupervised anomaly detection has been adopted by researchers more than other classification anomaly detection systems. Detection of anomalies using ML models is a promising area of research, and there are a lot of ML models that have been implemented by researchers. Therefore, we provide researchers with recommendations and guidelines based on this review.

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Machine Learning for Anomaly Detection: A Systematic Review

While operating in three different modes with bi-directional power flow, electricity theft detection, and localization in a grid-tied microgrid (MG) become challenging tasks, particularly when the occurrence rate of the theft and its frequency patterns are random. To address this problem, a stochastic Petri net formalism is used in this paper to detect and localize the occurrence of theft in grid-tied MGs. The disturbance at any instance of time in the form of resistance above a threshold in the accumulated data of a smart meter, irrespective of the mode of operation, triggers a transition assigned to the arc, which alerts the transmission module. The affected transition and suspected user information are forwarded to the meter data management system (MDMS) for localization of the theft. A smart meter forwards its readings to the MDMS only when a theft is detected; otherwise readings are sent outside an MG once or twice a day depending on utility regulations. This low sampling rate significantly improves the overall privacy of customers. For line resistance measurement in the MG, singular value decomposition is used. In experiments, it calculates the technical and non-technical losses with 100% accuracy without knowing the exact topology of the power distribution network.

Electricity Theft Detection and Localization in Grid-Tied Microgrids

Power utilities in different countries especially in the developing ones are incurring huge losses due to electricity theft. This paper proposes a prepaid energy metering system to control electricity theft. In this system a smart energy meter is installed in every consumer unit and a server is maintained at the service provider side. Both the meter and the server are equipped with GSM module which facilitates bidirectional communication between the two ends using the existing GSM infrastructure. Consumers can easily recharge their energy meter by sending a PIN number hidden in a scratch card to the server using SMS. This paper presents some measures to control meter bypassing and tampering. The bidirectional GSM communication using SMS ensures the effectiveness of these measures. Pilferage of electricity can be substantially reduced by incorporating the proposed measures along with the prepaid metering scheme. Legal actions against dishonest consumers can also be taken in this system.

A smart prepaid energy metering system to control electricity theft

Grid-tied solar inverters continue to proliferate rapidly to tackle the growing environmental challenges. Nowadays, different smart sensors and transducers are tightly integrated with the grid-tied inverter. This integration opens the "Pandora’s Box" of unknown threats that could come from very unconventional ways. This paper demonstrates a noninvasive attack that could come by spoofing the Hall sensor of an inverter in a stealthy way by using an external magnetic field. We demonstrate how an attacker can camouflage his/her attack tool and place it near a target inverter. In doing so, he/she can intentionally perturb grid voltage and frequency and can inject false real and reactive power to the grid. We also show the consequences of the attack on a scaled-down testbed of a power grid with a commercial 140 W grid-tied inverter from Texas Instruments. We are able to achieve a 31.52% change in output voltage, 3.16x (-6dB to -11dB) increase in lowfrequency harmonics power, and 3.44x increase in real power. Moreover, we introduce a duty-cycle variation approach for a noninvasive adversarial control that can change the inverter voltage up to 34% and real power up to 38%. We discuss the feasibility of using a 100 kW inverter through discussion. This provides insights behind the generalization of the attack model. In addition, the commercial power system simulation tool Etap 19.0.1 is used to simulate the impact of the attack on a 2.3 MW power grid. To the best of our knowledge, this is the first methodology that highlights the possibility of such an attack that might lead to grid blackout in a weak grid.

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Hall Spoofing: A Non-Invasive DoS Attack on Grid-Tied Solar Inverter

Since 2007, the use of side-channel measurements for detecting Hardware Trojan (HT) has been extensively studied. However, the majority of works either rely on a golden chip, or they rely on methods that are not robust against subtle acceptable changes that would occur over the life-cycle of an integrated circuit (IC). In this paper, we propose using a brain-inspired architecture called Hierarchical Temporal Memory (HTM) for HT detection. Similar to the human brain, our proposed solution is resilient against natural changes that might happen in the side-channel measurements while being able to accurately detect abnormal behavior of the chip when the HT gets triggered. We use a self-referencing method for HT detection, which eliminates the need for the golden chip. The effectiveness of our approach is evaluated using TrustHub benchmarks, which shows 92.20% detection accuracy on average.

Brain-Inspired Golden Chip Free Hardware Trojan Detection

https://cyberleninka.org/article/n/370599.pdf

Thermal Management in 3-D Integrated Circuits with Graphene Heat Spreaders

Micro-phasor measurement unit (μPMU) sensors in smart electric grids provide measurements of voltage and current at microsecond timescale across the network and have great potential value for grid diagnostics. In this work, we propose a novel neuro-cognitive inspired architecture based on Hierarchical Temporal Memory (HTM) for real-time anomaly detection in smart grid using μPMU data. The key technical idea is that the HTM learns a sparse distributed temporal representation of sequential data that turns out to be very useful for anomaly detection in real-time.Our numerical results show that the proposed HTM architecture can predict anomalies with 96%, 96%, and 98% accuracy for three different application profiles namely, Standard, Reward Few False Positive, Reward Few False Negative, respectively. The performance is compared with three state-of-the-art real-time anomaly detection algorithms and HTM demonstrates competitive score for real-time anomaly detection in μPMU data.

Anomadarshi Barua

Papers

A smart prepaid energy metering system to control electricity theft

Hall Spoofing: A Non-Invasive DoS Attack on Grid-Tied Solar Inverter

Brain-Inspired Golden Chip Free Hardware Trojan Detection

Thermal Management in 3-D Integrated Circuits with Graphene Heat Spreaders

Hierarchical Temporal Memory Based Machine Learning for Real-Time, Unsupervised Anomaly Detection in Smart Grid: WiP Abstract