Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes

Nandanoori, Sai Pushpak; Kundu, Soumya; Pal, Seemita; Agarwal, Khushbu; Choudhury, Sutanay

doi:10.1109/smartgridcomm47815.2020.9303022

Cited by 19 publications

(15 citation statements)

References 25 publications

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“…Nandanoori et al in [13] proposed a Koopman mode decomposition (KMD) based algorithm to detect and identify false data attacks in realtime. The Koopman modes (KMs) are capable of capturing the nonlinear modes of oscillation in the transient dynamics of the power networks and reveal the spatial embedding of both natural and anomalous modes of oscillations in the sensor measurements.…”

Section: Related Workmentioning

confidence: 99%

Malicious Node Detection in Smart Grid Networks

Yahmadi¹,

Ahmed²

2021

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Many countries around the world are implementing smart grids and smart meters. Malicious users that have moderate level of computer knowledge can manipulate smart meters and launch cyber-attacks. This poses cyber threats to network operators and government security. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we propose a model based on software that detects malicious nodes in a smart grid network. The model collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) model is implemented to classify nodes into good or malicious nodes by (high dimensional) giving the statues of 1 for good nodes and status of -1 for malicious (abnormal) nodes. The detection model also displays the network graphically as well as the data table. Moreover, this model displays the detection error in each cycle. It has a very low false alarm rate (2%) and a high detection rate as high as (98%). Future developments can trace the attack origin to eliminate or block the attack source minimizing losses before human control arrives.

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Section: Related Workmentioning

confidence: 99%

Malicious Node Detection in Smart Grid Networks

Yahmadi¹,

Ahmed²

2021

Computer Science &Amp; Information Technology (CS &Amp; IT)

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“…For the prediction step of KF, rather than using a physical model, we utilize Koopman Mode Analysis (KMA) [14] for fitting the model for prediction of behaviour of the system with a data-driven approach. A lot of application for KMA in power system has been developed including but not limited to power system stability and dynamic analysis [15], control and estimation [16], and fault line isolation [17] and attack detection [18]. There has been other studies of voltage stability-based analysis incorporating HVDC lines, including cyber and physical co-simulation in [19] which are also of high potential fits for KMA applications.…”

Section: B Related Workmentioning

confidence: 99%

Denoising and Bad Data Detection in Distribution Phasor Measurements using Filtering, Clustering and Koopman Mode Analysis

Vosughi¹,

Gholami²,

Srivastava³

2021

Preprint

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Distribution-level phasor measurement units (D-PMU) data are prone to different types of anomalies given complex data flow and processing infrastructure in an active power distribution system with enhanced digital automation. It is essential to pre-process the data before being used by critical applications for situational awareness and control. In this work, two approaches for detection of data anomalies are introduced for offline (larger data processing window) and online (shorter data processing window) applications. A margin-based maximum likelihood estimator (MB-MLE) method is developed to detect anomalies by integrating the results of different base detectors including Hampel filter, Quartile detector and DBSCAN. A smoothing wavelet denoising method is used to remove high-frequency noises. The processed data with offline analysis is used to fit a model to the underlying dynamics of synchrophasor data using Koopman Mode Analysis, which is subsequently employed for online denoising and bad data detection (BDD) using Kalman Filter (KF). The parameters of the KF are adjusted adaptively based on similarity to the training data set for model fitting purposes. Developed techniques have been validated for the modified IEEE test system with multiple D-PMUs, modeled and simulated in real-time for different case scenarios using the OPAL-RT Hardware-In-the-Loop (HIL) Simulator.

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“…Adversarial attacks on power grids comprise of false-data injection, jamming, DoS and packet-dropping attacks [9,10,15]. While researchers have proposed a multitude of defense mechanisms [27], including Moving Target Defense (MTDs) [7,20], they do not consider the problem of sensor placement to monitor HVTs.…”

Section: Related Workmentioning

confidence: 99%

“…identify the source of the anomaly by utilizing the sensor readings generated by PMUs. With the continuous discovery of real-world attacks such as Stuxnet [13], Dragonfly [28] and a wide range of cyberattacks-jamming, Denial of Service, packet dropping, false-data injection and compromise of data integrity [15,16]-robustness of existing sensor placement mechanisms becomes critical. Thus, in this work, we leverage the ideas of Moving Target Defense (MTD) in cybersecurity [12,25] and the Minimum Discriminating Code Set (MDCS) based PMU placement [3,4] to build a defense-in-depth solution.…”

Section: Introductionmentioning

confidence: 99%

Moving Target Defense for Robust Monitoring of Electric Grid Transformers in Adversarial Environments

Sengupta¹,

Basu²,

Sen³

et al. 2020

Preprint

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Electric power grid components, such as high voltage transformers (HVTs), generating stations, substations, etc. are expensive to maintain and, in the event of failure, replace. Thus, regularly monitoring the behavior of such components is of utmost importance. Furthermore, the recent increase in the number of cyberattacks on such systems demands that such monitoring strategies should be robust. In this paper, we draw inspiration from work in Moving Target Defense (MTD) and consider a dynamic monitoring strategy that makes it difficult for an attacker to prevent unique identification of behavioral signals that indicate the status of HVTs. We first formulate the problem of finding a differentially immune configuration set for an MTD in the context of power grids and then propose algorithms to compute it. To find the optimal movement strategy, we model the MTD as a two-player game and consider the Stackelberg strategy. With the help of IEEE test cases, we show the efficacy and scalability of our proposed approaches. 1

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Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes

Cited by 19 publications

References 25 publications

Malicious Node Detection in Smart Grid Networks

Malicious Node Detection in Smart Grid Networks

Denoising and Bad Data Detection in Distribution Phasor Measurements using Filtering, Clustering and Koopman Mode Analysis

Moving Target Defense for Robust Monitoring of Electric Grid Transformers in Adversarial Environments

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