Data‐driven detection and identification of IoT‐enabled load‐altering attacks in power grids

Abstract

Advances in edge computing are powering the development and deployment of Internet of Things (IoT) systems to provide advanced services and resource efficiency. However, large‐scale IoT‐based load‐altering attacks (LAAs) can seriously impact power grid operations, such as destabilising the grid's control loops. Timely detection and identification of any compromised nodes are essential to minimise the adverse effects of these attacks on power grid operations. In this work, two data‐driven algorithms are proposed to detect and identify compromised nodes and the attack parameters of the LAAs. The first method, based on the Sparse Identification of Nonlinear Dynamics approach, adopts a sparse regression framework to identify attack parameters that best describe the observed dynamics. The second method, based on physics‐informed neural networks, employs neural networks to infer the attack parameters from the measurements. Both algorithms are presented utilising edge computing for deployment over decentralised architectures. Extensive simulations are performed on IEEE 6‐, 14‐, and 39‐bus systems to verify the effectiveness of the proposed methods. Numerical results confirm that the proposed algorithms outperform existing approaches, such as those based on unscented Kalman filter, support vector machines, and neural networks (NN), and effectively detect and identify locations of attack in a timely manner.

Identification of Anomaly Detection in Power System State Estimation Based on Fuzzy C-Means Algorithm

“…In formula (12) as the number of clusters n of measurement data decreases monotonously, there is an infection point n 0 . In this way, when the number of measured samples m changes, the number of iterations of the algorithm will change, and the minimum value is taken near the infection point n 0 , namely,…”

“…Set formula (12) as the objective function, formula (13), formula (15), and formula (16) as the constraints, and when the error of the two previous and subsequent iterations of the objective function is less than a given positive number after a certain number of iterations, the clustering ends.…”

“…Te vertical quartile and composite machine learning are combined to identify the abnormal points of wind speed and power data of wind farms. Reference [12] proposed a data-driven detection technology to detect abnormal attack data in the power grid. In the research, the sparse identifcation method based on nonlinear dynamics and the neural network based on physical knowledge are used to detect the attack parameters in the power grid, and the simulation in IEEE 6 and other systems shows that this method can efectively detect the abnormal attack data in the power grid.…”

A Deep-Learning-Based Solution for Securing the Power Grid Against Load Altering Threats by IoT-Enabled Devices

“…Detection and localization: The focus of this work is on developing a fast and robust tool to detect and localize D-LAAs with high precision, which is an essential step toward limiting the damage due to D-LAAs. To this end, existing works have proposed utilizing the phase angle/frequency dynamics monitored by phasor measurement units (PMUs) [8]- [10]. Similar approaches using PMU measurement data are also adopted in the broader context of detecting cyberphysical attacks against wide-area monitoring systems [11], [12], localizing the source of faults/oscillations [13]- [15] and disturbance type classification [16].…”

Analysis of Load-Altering Attacks Against Power Grids: A Rare-Event Sampling Approach

“…Moreover, dynamic LAAs, in which the attacker injects a series of load perturbations over time, can also destabilize the power grid's frequency control loop [7]. Subsequent work has also focused on detecting LAAs using data-driven approaches based on the data gathered from phasor measurement units [9], [10].…”