skip to main content

Title: Noise Resilient Learning for Attack Detection in Smart Grid PMU Infrastructure
Falsified data from compromised Phasor Measurement Units (PMUs) in a smart grid induce Energy Management Systems (EMS) to have an inaccurate estimation of the state of the grid, disrupting various operations of the power grid. Moreover, the PMUs deployed at the distribution layer of a smart grid show dynamic fluctuations in their data streams, which make it extremely challenging to design effective learning frameworks for anomaly based attack detection. In this paper, we propose a noise resilient learning framework for anomaly based attack detection specifically for distribution layer PMU infrastructure, that show real time indicators of data falsifications attacks while offsetting the effect of false alarms caused by the noise. Specifically, we propose a feature extraction framework that uses some Pythagorean Means of the active power from a cluster of PMUs, reducing multi-dimensional nature of the PMU data streams via quick big data summarization. We also propose a robust and noise resilient methodology for learning thresholds based on generalized robust estimation theory of our invariant feature. We experimentally validate our approach and demonstrate improved reliability performance using two completely different datasets collected from real distribution level PMU infrastructures.  more » « less
Award ID(s):
2030611 1818942
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
IEEE Transactions on Dependable and Secure Computing
Page Range / eLocation ID:
1 to 17
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. The applications for wide area monitoring, protection, and control systems (WAMPC) at the control center, help with providing resilient, efficient, and secure operation of the transmission system of the smart grid. The increased proliferation of phasor measurement units (PMUs) in this space has inspired many prudent applications to assist in the process of decision making in the control centers. Machine learning (ML) based decision support systems have become viable with the availability of abundant high-resolution wide area operational PMU data. We propose a deep neural network (DNN) based supervisory protection and event diagnosis system and demonstrate that it works with very high degree of confidence. The system introduces a supervisory layer that processes the data streams collected from PMUs and detects disturbances in the power systems that may have gone unnoticed by the local monitoring and protection system. Then, we investigate compromise of the insights of this ML based supervisory control by crafting adversaries that corrupt the PMU data via minimal coordinated manipulation and identification of the spatio-temporal regions in the multidimensional PMU data in a way that the DNN classifier makes wrong event predictions. This dataset contains images that represent PMU data described in the reference paper. Each image has a dimension of [300X20X3] comprising of 300 time points, 10 voltage and 10 frequency measurements, and 3 fundamental color intensities. Each of the image represents the instance of a disturbance. We consider a disturbance pattern length of 5s, with 0.5 s before the trigger and 4.5 s after the trigger. Voltage and frequency data streams from 10 PMUs at a sampling rate of 60 frames per second, were aggregated to form these pseudo color images. The data-set consisted of three sub-folders: 1. 344 instances of faults located in the sub-folder “DB_FLT” 2. 140 instances of loss of generation located in the sub-folder “DB_GNL” 3. 21 instances of synchronous motor switching events located in the sub-folder “DB_SMS”. 
    more » « less
  2. The push to automate and digitize the electric grid has led to widespread installation of Phasor Measurement Units (PMUs) for improved real-time wide-area system monitoring and control. Nevertheless, transforming large volumes of highresolution PMU measurements into actionable insights remains challenging. A central challenge is creating flexible and scalable online anomaly detection in PMU data streams. PMU data can hold multiple types of anomalies arising in the physical system or the cyber system (measurements and communication networks). Increasing the grid situational awareness for noisy measurement data and Bad Data (BD) anomalies has become more and more significant. Number of machine learning, data analytics and physics based algorithms have been developed for anomaly detection, but need to be validated with realistic synchophasor data. Access to field data is very challenging due to confidentiality and security reasons. This paper presents a method for generating realistic synchrophasor data for the given synthetic network as well as event and bad data detection and classification algorithms. The developed algorithms include Bayesian and change-point techniques to identify anomalies, a statistical approach for event localization and multi-step clustering approach for event classification. Developed algorithms have been validated with satisfactory results for multiple examples of power system events including faults and load/generator/capacitor variations/switching for an IEEE test system. Set of synchrophasor data will be available publicly for other researchers. 
    more » « less
  3. null (Ed.)
    Spurious power consumption data reported from compromised meters controlled by organized adversaries in the Advanced Metering Infrastructure (AMI) may have drastic consequences on a smart grid’s operations. While existing research on data falsification in smart grids mostly defends against isolated electricity theft, we introduce a taxonomy of various data falsification attack types, when smart meters are compromised by organized or strategic rivals. To counter these attacks, we first propose a coarse-grained and a fine-grained anomaly-based security event detection technique that uses indicators such as deviation and directional change in the time series of the proposed anomaly detection metrics to indicate: (i) occurrence, (ii) type of attack, and (iii) attack strategy used, collectively known as attack context . Leveraging the attack context information, we propose three attack response metrics to the inferred attack context: (a) an unbiased mean indicating a robust location parameter; (b) a median absolute deviation indicating a robust scale parameter; and (c) an attack probability time ratio metric indicating the active time horizon of attacks. Subsequently, we propose a trust scoring model based on Kullback-Leibler (KL) divergence, that embeds the appropriate unbiased mean, the median absolute deviation, and the attack probability ratio metric at runtime to produce trust scores for each smart meter. These trust scores help classify compromised smart meters from the non-compromised ones. The embedding of the attack context, into the trust scoring model, facilitates accurate and rapid classification of compromised meters, even under large fractions of compromised meters, generalize across various attack strategies and margins of false data. Using real datasets collected from two different AMIs, experimental results show that our proposed framework has a high true positive detection rate, while the average false alarm and missed detection rates are much lesser than 10% for most attack combinations for two different real AMI micro-grid datasets. Finally, we also establish fundamental theoretical limits of the proposed method, which will help assess the applicability of our method to other domains. 
    more » « less
  4. null (Ed.)
    Smart grids integrate advanced information and communication technologies (ICTs) into traditional power grids for more efficient and resilient power delivery and management, but also introduce new security vulnerabilities that can be exploited by adversaries to launch cyber attacks, causing severe consequences such as massive blackout and infrastructure damages. Existing machine learning-based methods for detecting cyber attacks in smart grids are mostly based on supervised learning, which need the instances of both normal and attack events for training. In addition, supervised learning requires that the training dataset includes representative instances of various types of attack events to train a good model, which is sometimes hard if not impossible. This paper presents a new method for detecting cyber attacks in smart grids using PMU data, which is based on semi-supervised anomaly detection and deep representation learning. Semi-supervised anomaly detection only employs the instances of normal events to train detection models, making it suitable for finding unknown attack events. A number of popular semi-supervised anomaly detection algorithms were investigated in our study using publicly available power system cyber attack datasets to identify the best-performing ones. The performance comparison with popular supervised algorithms demonstrates that semi-supervised algorithms are more capable of finding attack events than supervised algorithms. Our results also show that the performance of semi-supervised anomaly detection algorithms can be further improved by augmenting with deep representation learning. 
    more » « less
  5. null (Ed.)
    Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. The proposed methodology uses dilated convolution and long short-term memory (LSTM) layers to learn temporal as well as long term dependencies within sensor and actuator data in an ICS. The sensor/actuator data are passed through a unique feature engineering pipeline where wavelet transformation is applied to the sensor signals to extract features that are fed into the model. Additionally, this paper explores four variations of supervised deep learning models, as well as an unsupervised support vector machine (SVM) model for this problem. The proposed framework is validated on Secure Water Treatment testbed results. This framework detects more attacks in a shorter period of time than previously published methods. 
    more » « less