The Experts below are selected from a list of 6048 Experts worldwide ranked by ideXlab platform
Sindhu Suresh - One of the best experts on this subject based on the ideXlab platform.
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cyber physical Security Analytics for transactive energy systems
2020Co-Authors: Yue Zhang, V V G Krishnan, K Kaur, Anurag K Srivastava, Adam Hahn, Sindhu SureshAbstract:With the significant increase in integration of renewable energy generation into the electric grid, market-based transactive exchanges between energy producers and prosumers will become more common. Transactive energy systems (TESs) employ economic and control mechanisms to dynamically balance the demand and supply across the electrical grid. Emerging transactive control mechanism depends on a large number of distributed edge-computing and Internet of Things (IoT) devices making autonomous/semi-autonomous decisions on energy production, and demand response. However, the electric grid cyber assets and the IoT devices are increasingly vulnerable to attack. TES will likely have similar vulnerabilities and cyber attacks especially with financial interest motives of stakeholders, which could affect the operation of the power grid. Therefore, new analytical methods are needed to continuously monitor these systems operations and detect malicious activity. In this research work, various components of transactive energy systems are modeled and simulated in detail. Various cyber attack models are developed based on threats identified in TES. A deep learning approach called deep stacked autoencoder (SAE) is utilized to detect possible anomalies in the market and physical system measurements. The proposed unsupervised technique is validated for satisfactory performance to detect anomalies and trigger a further investigation for root cause analysis using end-to-end TES testbed and use case.
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Cyber Physical Security Analytics for Anomalies in Transmission Protection Systems
2019Co-Authors: A. Ahmed, V V G Krishnan, Anurag K Srivastava, Adam Hahn, S. A. Foroutan, Y. Wu, Touhiduzzaman, Caroline Rublein, Sindhu SureshAbstract:Protection systems are one of the most critical components in the transmission system and are becoming more digital with ongoing automation. These digital systems are prone to vulnerabilities/attacks, and exploitation of these vulnerabilities may cause major impacts on the electric grid performance. Multiple alarms reported in the control center could be a result of the faults (expected operations) or failures in the protection system (anomalies/ unexpected operation). Situational awareness gained through sensors such as a phasor measurement unit (PMU) and data acquired through the cyber system provide an opportunity to develop continuous cyber-physical monitoring of the system. Note that relay data are not reported in the control center continuously. This paper presents a cyber-physical data Analytics based technique to monitor transmission protection system and detect malicious activity. Initially, continuous monitoring of PMU data is utilized for data anomaly detection, which includes bad or missing data using long short-term memory (LSTM). Then, PMU data of interest are utilized for failure diagnosis, using a semisupervised deep autoencoder model. In this research, cyber anomalies are modeled by manipulating the setting/logic design of protective devices, and a ridge regression based classifier with a feature engineering pipeline is used to detect cyber anomalies. The results from the deep autoencoder model and ridge regression based classifier are then utilized for detailed investigation to find the root causes of the observed events assisted by the cyber log data from the protection devices. The algorithm is validated using a real-time simulation of the IEEE test system with industrial hardware relays and PMUs in the loop. Data Analytics algorithm running on server utilizes these real-time data continuously for anomaly detection and classification for the developed use cases.
V V G Krishnan - One of the best experts on this subject based on the ideXlab platform.
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cyber physical Security Analytics for transactive energy systems
2020Co-Authors: Yue Zhang, V V G Krishnan, K Kaur, Anurag K Srivastava, Adam Hahn, Sindhu SureshAbstract:With the significant increase in integration of renewable energy generation into the electric grid, market-based transactive exchanges between energy producers and prosumers will become more common. Transactive energy systems (TESs) employ economic and control mechanisms to dynamically balance the demand and supply across the electrical grid. Emerging transactive control mechanism depends on a large number of distributed edge-computing and Internet of Things (IoT) devices making autonomous/semi-autonomous decisions on energy production, and demand response. However, the electric grid cyber assets and the IoT devices are increasingly vulnerable to attack. TES will likely have similar vulnerabilities and cyber attacks especially with financial interest motives of stakeholders, which could affect the operation of the power grid. Therefore, new analytical methods are needed to continuously monitor these systems operations and detect malicious activity. In this research work, various components of transactive energy systems are modeled and simulated in detail. Various cyber attack models are developed based on threats identified in TES. A deep learning approach called deep stacked autoencoder (SAE) is utilized to detect possible anomalies in the market and physical system measurements. The proposed unsupervised technique is validated for satisfactory performance to detect anomalies and trigger a further investigation for root cause analysis using end-to-end TES testbed and use case.
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Cyber Physical Security Analytics for Anomalies in Transmission Protection Systems
2019Co-Authors: A. Ahmed, V V G Krishnan, Anurag K Srivastava, Adam Hahn, S. A. Foroutan, Y. Wu, Touhiduzzaman, Caroline Rublein, Sindhu SureshAbstract:Protection systems are one of the most critical components in the transmission system and are becoming more digital with ongoing automation. These digital systems are prone to vulnerabilities/attacks, and exploitation of these vulnerabilities may cause major impacts on the electric grid performance. Multiple alarms reported in the control center could be a result of the faults (expected operations) or failures in the protection system (anomalies/ unexpected operation). Situational awareness gained through sensors such as a phasor measurement unit (PMU) and data acquired through the cyber system provide an opportunity to develop continuous cyber-physical monitoring of the system. Note that relay data are not reported in the control center continuously. This paper presents a cyber-physical data Analytics based technique to monitor transmission protection system and detect malicious activity. Initially, continuous monitoring of PMU data is utilized for data anomaly detection, which includes bad or missing data using long short-term memory (LSTM). Then, PMU data of interest are utilized for failure diagnosis, using a semisupervised deep autoencoder model. In this research, cyber anomalies are modeled by manipulating the setting/logic design of protective devices, and a ridge regression based classifier with a feature engineering pipeline is used to detect cyber anomalies. The results from the deep autoencoder model and ridge regression based classifier are then utilized for detailed investigation to find the root causes of the observed events assisted by the cyber log data from the protection devices. The algorithm is validated using a real-time simulation of the IEEE test system with industrial hardware relays and PMUs in the loop. Data Analytics algorithm running on server utilizes these real-time data continuously for anomaly detection and classification for the developed use cases.
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Cyber Physical Security Analytics For Transactive Energy Systems Using Ensemble Machine Learning
2018Co-Authors: A. Arman, V V G Krishnan, Anurag K Srivastava, Y. Wu, S. SindhuAbstract:Demand response and active participation of end-users (prosumers) are expected to play a critical role in the future power grids. Market based transactive exchanges between prosumers are triggered by the increased deployments of renewable generations and microgrid architectures. Transactive Energy Systems (TES) employ economic and control mechanisms to dynamically balance the demand and supply across the electrical grid. Effective transactive mechanisms leverage on a large number of distributed edge-computing and a communication architecture. Given the prolific usage of digital devices, the assets within a transactive environment are vulnerable to various threats. This paper utilizes a machine learning technique to detect possible anomalies within a transactive energy framework. An ensemble based methodology is used to detect anomalies in the market and physical system measurements. The proposed technique is validated for satisfactory performance to detect anomalies and trigger further investigation for root cause analysis and mitigation.
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Cyber Physical Security Analytics for Anomalies in Transmission Protection Systems
2018Co-Authors: A. Ahmed, V V G Krishnan, Adam Hahn, S. A. Foroutan, M. Touhiduzzaman, A. Srivastava, Y. Wu, S. SindhuAbstract:Protection devices are considered to be the most critical components responsible for protecting the electrical grid. Due to recent technological advancements in the electrical grid, digitalization has played an influential role in integration of digital devices in protection systems. Incorporation of digital devices in protection systems has made Transmission Protection System more prone to vulnerabilities and cyber-attacks. A cyber attack exploiting protection devices aims to disrupt the normal operations by raising multiple false alarms on a large scale creating conflicting and confusing observation in the control center. Finding exact root cause(s) for the multiple alarms is important to solve this problem. The research presented in this paper imitates a cyber attack on the IEEE test system with industrial hardware relays in the loop, by manipulating the setting/logic design of protection devices in the system creating conflicting alarms in the control center. This paper presents a novel data Analytics based approach combining signature-based method for detecting an intrusion in the cyber system and a deep learning algorithm for detecting a mal-operation in the physical system. Data gathered from the physical system through sensors such as Phasor Measurement Units (PMUs) and data acquired from cyber system through relay are analyzed by data Analytics approach finding the root-cause of the observed events. The results of data Analytics are further validated using the log data from protection devices.
Anurag K Srivastava - One of the best experts on this subject based on the ideXlab platform.
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cyber physical Security Analytics for transactive energy systems
2020Co-Authors: Yue Zhang, V V G Krishnan, K Kaur, Anurag K Srivastava, Adam Hahn, Sindhu SureshAbstract:With the significant increase in integration of renewable energy generation into the electric grid, market-based transactive exchanges between energy producers and prosumers will become more common. Transactive energy systems (TESs) employ economic and control mechanisms to dynamically balance the demand and supply across the electrical grid. Emerging transactive control mechanism depends on a large number of distributed edge-computing and Internet of Things (IoT) devices making autonomous/semi-autonomous decisions on energy production, and demand response. However, the electric grid cyber assets and the IoT devices are increasingly vulnerable to attack. TES will likely have similar vulnerabilities and cyber attacks especially with financial interest motives of stakeholders, which could affect the operation of the power grid. Therefore, new analytical methods are needed to continuously monitor these systems operations and detect malicious activity. In this research work, various components of transactive energy systems are modeled and simulated in detail. Various cyber attack models are developed based on threats identified in TES. A deep learning approach called deep stacked autoencoder (SAE) is utilized to detect possible anomalies in the market and physical system measurements. The proposed unsupervised technique is validated for satisfactory performance to detect anomalies and trigger a further investigation for root cause analysis using end-to-end TES testbed and use case.
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Cyber Physical Security Analytics for Anomalies in Transmission Protection Systems
2019Co-Authors: A. Ahmed, V V G Krishnan, Anurag K Srivastava, Adam Hahn, S. A. Foroutan, Y. Wu, Touhiduzzaman, Caroline Rublein, Sindhu SureshAbstract:Protection systems are one of the most critical components in the transmission system and are becoming more digital with ongoing automation. These digital systems are prone to vulnerabilities/attacks, and exploitation of these vulnerabilities may cause major impacts on the electric grid performance. Multiple alarms reported in the control center could be a result of the faults (expected operations) or failures in the protection system (anomalies/ unexpected operation). Situational awareness gained through sensors such as a phasor measurement unit (PMU) and data acquired through the cyber system provide an opportunity to develop continuous cyber-physical monitoring of the system. Note that relay data are not reported in the control center continuously. This paper presents a cyber-physical data Analytics based technique to monitor transmission protection system and detect malicious activity. Initially, continuous monitoring of PMU data is utilized for data anomaly detection, which includes bad or missing data using long short-term memory (LSTM). Then, PMU data of interest are utilized for failure diagnosis, using a semisupervised deep autoencoder model. In this research, cyber anomalies are modeled by manipulating the setting/logic design of protective devices, and a ridge regression based classifier with a feature engineering pipeline is used to detect cyber anomalies. The results from the deep autoencoder model and ridge regression based classifier are then utilized for detailed investigation to find the root causes of the observed events assisted by the cyber log data from the protection devices. The algorithm is validated using a real-time simulation of the IEEE test system with industrial hardware relays and PMUs in the loop. Data Analytics algorithm running on server utilizes these real-time data continuously for anomaly detection and classification for the developed use cases.
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Cyber Physical Security Analytics For Transactive Energy Systems Using Ensemble Machine Learning
2018Co-Authors: A. Arman, V V G Krishnan, Anurag K Srivastava, Y. Wu, S. SindhuAbstract:Demand response and active participation of end-users (prosumers) are expected to play a critical role in the future power grids. Market based transactive exchanges between prosumers are triggered by the increased deployments of renewable generations and microgrid architectures. Transactive Energy Systems (TES) employ economic and control mechanisms to dynamically balance the demand and supply across the electrical grid. Effective transactive mechanisms leverage on a large number of distributed edge-computing and a communication architecture. Given the prolific usage of digital devices, the assets within a transactive environment are vulnerable to various threats. This paper utilizes a machine learning technique to detect possible anomalies within a transactive energy framework. An ensemble based methodology is used to detect anomalies in the market and physical system measurements. The proposed technique is validated for satisfactory performance to detect anomalies and trigger further investigation for root cause analysis and mitigation.
Adam Hahn - One of the best experts on this subject based on the ideXlab platform.
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cyber physical Security Analytics for transactive energy systems
2020Co-Authors: Yue Zhang, V V G Krishnan, K Kaur, Anurag K Srivastava, Adam Hahn, Sindhu SureshAbstract:With the significant increase in integration of renewable energy generation into the electric grid, market-based transactive exchanges between energy producers and prosumers will become more common. Transactive energy systems (TESs) employ economic and control mechanisms to dynamically balance the demand and supply across the electrical grid. Emerging transactive control mechanism depends on a large number of distributed edge-computing and Internet of Things (IoT) devices making autonomous/semi-autonomous decisions on energy production, and demand response. However, the electric grid cyber assets and the IoT devices are increasingly vulnerable to attack. TES will likely have similar vulnerabilities and cyber attacks especially with financial interest motives of stakeholders, which could affect the operation of the power grid. Therefore, new analytical methods are needed to continuously monitor these systems operations and detect malicious activity. In this research work, various components of transactive energy systems are modeled and simulated in detail. Various cyber attack models are developed based on threats identified in TES. A deep learning approach called deep stacked autoencoder (SAE) is utilized to detect possible anomalies in the market and physical system measurements. The proposed unsupervised technique is validated for satisfactory performance to detect anomalies and trigger a further investigation for root cause analysis using end-to-end TES testbed and use case.
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Cyber Physical Security Analytics for Anomalies in Transmission Protection Systems
2019Co-Authors: A. Ahmed, V V G Krishnan, Anurag K Srivastava, Adam Hahn, S. A. Foroutan, Y. Wu, Touhiduzzaman, Caroline Rublein, Sindhu SureshAbstract:Protection systems are one of the most critical components in the transmission system and are becoming more digital with ongoing automation. These digital systems are prone to vulnerabilities/attacks, and exploitation of these vulnerabilities may cause major impacts on the electric grid performance. Multiple alarms reported in the control center could be a result of the faults (expected operations) or failures in the protection system (anomalies/ unexpected operation). Situational awareness gained through sensors such as a phasor measurement unit (PMU) and data acquired through the cyber system provide an opportunity to develop continuous cyber-physical monitoring of the system. Note that relay data are not reported in the control center continuously. This paper presents a cyber-physical data Analytics based technique to monitor transmission protection system and detect malicious activity. Initially, continuous monitoring of PMU data is utilized for data anomaly detection, which includes bad or missing data using long short-term memory (LSTM). Then, PMU data of interest are utilized for failure diagnosis, using a semisupervised deep autoencoder model. In this research, cyber anomalies are modeled by manipulating the setting/logic design of protective devices, and a ridge regression based classifier with a feature engineering pipeline is used to detect cyber anomalies. The results from the deep autoencoder model and ridge regression based classifier are then utilized for detailed investigation to find the root causes of the observed events assisted by the cyber log data from the protection devices. The algorithm is validated using a real-time simulation of the IEEE test system with industrial hardware relays and PMUs in the loop. Data Analytics algorithm running on server utilizes these real-time data continuously for anomaly detection and classification for the developed use cases.
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Cyber Physical Security Analytics for Anomalies in Transmission Protection Systems
2018Co-Authors: A. Ahmed, V V G Krishnan, Adam Hahn, S. A. Foroutan, M. Touhiduzzaman, A. Srivastava, Y. Wu, S. SindhuAbstract:Protection devices are considered to be the most critical components responsible for protecting the electrical grid. Due to recent technological advancements in the electrical grid, digitalization has played an influential role in integration of digital devices in protection systems. Incorporation of digital devices in protection systems has made Transmission Protection System more prone to vulnerabilities and cyber-attacks. A cyber attack exploiting protection devices aims to disrupt the normal operations by raising multiple false alarms on a large scale creating conflicting and confusing observation in the control center. Finding exact root cause(s) for the multiple alarms is important to solve this problem. The research presented in this paper imitates a cyber attack on the IEEE test system with industrial hardware relays in the loop, by manipulating the setting/logic design of protection devices in the system creating conflicting alarms in the control center. This paper presents a novel data Analytics based approach combining signature-based method for detecting an intrusion in the cyber system and a deep learning algorithm for detecting a mal-operation in the physical system. Data gathered from the physical system through sensors such as Phasor Measurement Units (PMUs) and data acquired from cyber system through relay are analyzed by data Analytics approach finding the root-cause of the observed events. The results of data Analytics are further validated using the log data from protection devices.
Yue Zhang - One of the best experts on this subject based on the ideXlab platform.
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cyber physical Security Analytics for transactive energy systems
2020Co-Authors: Yue Zhang, V V G Krishnan, K Kaur, Anurag K Srivastava, Adam Hahn, Sindhu SureshAbstract:With the significant increase in integration of renewable energy generation into the electric grid, market-based transactive exchanges between energy producers and prosumers will become more common. Transactive energy systems (TESs) employ economic and control mechanisms to dynamically balance the demand and supply across the electrical grid. Emerging transactive control mechanism depends on a large number of distributed edge-computing and Internet of Things (IoT) devices making autonomous/semi-autonomous decisions on energy production, and demand response. However, the electric grid cyber assets and the IoT devices are increasingly vulnerable to attack. TES will likely have similar vulnerabilities and cyber attacks especially with financial interest motives of stakeholders, which could affect the operation of the power grid. Therefore, new analytical methods are needed to continuously monitor these systems operations and detect malicious activity. In this research work, various components of transactive energy systems are modeled and simulated in detail. Various cyber attack models are developed based on threats identified in TES. A deep learning approach called deep stacked autoencoder (SAE) is utilized to detect possible anomalies in the market and physical system measurements. The proposed unsupervised technique is validated for satisfactory performance to detect anomalies and trigger a further investigation for root cause analysis using end-to-end TES testbed and use case.
