Cyber physical security analytics for transactive energy systems using ensemble machine learning

Arman, Arry Akhmad; Krishnan, V. V. G.; Srivastava, A.; Wu, Yinghui; Sindhu, S.

doi:10.1109/naps.2018.8600639

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…Furthermore, intend to examine and analyze the impact of additional attacks, such as DoS and replay attacks, on the microgrid's peer-to-peer markets, as well as deploy detection schemes in the microgrid considered for future work. In [61], researchers have presented an ensemble decision tree approach based on the bagging technique to find possible anomalies in the electricity market and physical measurements within the Transactive Energy System (TES), which can reduce the impact of outliers. In addition, the presented approach may be tested on advanced use scenarios to depict a few realistic TES behaviors.…”

Section: Review On Cyber-attacks In Power Distribution Systemmentioning

confidence: 99%

“…Many researchers published various analysis methods, detection, and protection techniques in the literature to control the communication attacks. In [89], researchers reviewed about cyber systems and cyber physical systems, as [65] Western Electric Co-ordinating Council 240 -Bus model [35] High Fidelity Simulation Test -Bed [36] NPCC 140 -Bus System [62] TESP & IEEE 9 -Bus System [66] TESP & IEEE 9 -Bus System [67] TESP & IEEE 9 -Bus System [61] TESP & IEEE 9 -Bus System [84] OPNET Simulator [85] SUMO & OMNET ++ [86] Speed Goat Real-Time Digital Simulator [? ]…”

Section: Attacks Through Communication Channelsmentioning

confidence: 99%

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A Comprehensive Review on Cyber-Attacks in Power Systems: Impact Analysis, Detection, and Cyber Security

Tatipatri,

Arun

2024

IEEE Access

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Continuous communication and information technology advancements facilitate the modernization of the conventional energy grid into an integrated platform. Internet-of-Things (IoT) incorporates power systems, particularly smart grid features and the delivery of new services from the utility side to the end user over a two-way communication channel. However, severe security vulnerabilities have been created due to over-dependency on IoT based communication systems. In addition, critical information exchange between any two entities or devices is always an appealing target for cyber-attackers, especially with financial interest motive by damaging integrity, confidentiality and authenticity in a communication channel. Maintaining data security and preserving privacy in between two entities during the transmission or any data distribution are essential. The potential attacks and impacts of those attacks need to be investigated to develop an effective cyber security infrastructure. Thus, considerable researchers focused on detection and mitigation of these vulnerable cyber-attacks using advanced computation tools. This review article thoroughly investigated possible ways to address cyber security challenges such as smart meter security, end-users privacy, electricity theft cyber-attacks using blockchain and cryptography against communication attacks in smart grid. The operational impacts of cyber-attacks on power system security, as well as the economic impact on deregulated energy markets, have been extensively explored. In addition, the robustness of security features and cryptographic methods against various cyber-attacks is investigated to suggest unexplored cyber-attacks for future scope. Specially, the study of real-world cyber security events, case studies, new findings and new scopes in diverse power industries are carried out. More than 135 research articles has been examined for this review article. This paper mainly concentrates on distribution-side cyberattacks with impact analysis, detection and protection techniques.

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Section: Review On Cyber-attacks In Power Distribution Systemmentioning

confidence: 99%

Section: Attacks Through Communication Channelsmentioning

confidence: 99%

A Comprehensive Review on Cyber-Attacks in Power Systems: Impact Analysis, Detection, and Cyber Security

Tatipatri,

Arun

2024

IEEE Access

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“…In particular, the consensus mechanism is of interest as we also propose an unsupervised ensemble method. Arman et al [ 32 ] present an ensemble OD method where multiple decision trees are trained on a subset of the data set to differentiate outliers in simulated data of a CPS network. For consensus, each tree is weighted with a certain confidence level, and the vote multiplied by the confidence level is averaged per class and vote.…”

Section: Related Workmentioning

confidence: 99%

Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis

Kaupp

Humm

Nazemi

et al. 2022

Sensors

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Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert.

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“…The thresholds are compared with the new data to detect anomalous behaviours. An ensemble learning model that combines the results from multiple decision tress was proposed in [23] for a Transactive Energy Systems (TES). TES is an essential part of the smart grid that monitors the information transactions between various stakeholders in the grid to make decisions on energy consumption and demand response.…”

Section: Literature Survey On Machine Learning (Ml) Approaches For Anomaly Detection In Smart Gridmentioning

confidence: 99%

An Introductory Review Of Anomaly Detection Methods In Smart Grids

Preethi¹,

K²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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Cyber Physical systems such as smart grids have the potential to address the future energy crisis. Because of the bidirectional flow of information across various domains in a smart grid, anomaly detection is one of the prime security related challenges. Machine learning models have emerged as one of the prospective artificial intelligence technologies to model supervised and unsupervised data for analysis and prediction. This paper reviews the various anomaly detection schemes in a Smart Grid Infrastructure based on machine learning techniques.

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Cyber physical security analytics for transactive energy systems using ensemble machine learning

Cited by 10 publications

References 12 publications

A Comprehensive Review on Cyber-Attacks in Power Systems: Impact Analysis, Detection, and Cyber Security

A Comprehensive Review on Cyber-Attacks in Power Systems: Impact Analysis, Detection, and Cyber Security

Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis

An Introductory Review Of Anomaly Detection Methods In Smart Grids

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