scholarly journals An Integrated Control and Intrusion Detection System for Smart Grid Security

2018 ◽  
pp. 243-264
Author(s):  
Eniye Tebekaemi ◽  
Duminda Wijesekera ◽  
Paulo Costa
Keyword(s):  
Intrusion Detection ◽  
Smart Grid ◽  
Intrusion Detection System ◽  
Detection System ◽  
Integrated Control ◽  
Grid Security ◽  
Smart Grid Security

scholarly journals An Intrusion Detection System for Smart Grid Neighborhood Area Network

2021 ◽  
Author(s):  
Nasim Beigi Mohammadi
Keyword(s):  
Intrusion Detection ◽  
Smart Grid ◽  
Intrusion Detection System ◽  
Detection System ◽  
Area Network ◽  
Wormhole Attack ◽  
Detection Systems ◽  
Wireless Mesh ◽  
First Time ◽  
Neighborhood Area Network

Smart grid is expected to improve the efficiency, reliability and economics of current energy systems. Using two-way flow of electricity and information, smart grid builds an automated, highly distributed energy delivery network. In this thesis, we present the requirements for intrusion detection systems in smart grid, neighborhood area network (NAN) in particular. We propose an intrusion detection system (IDS) that considers the constraints and requirements of the NAN. It captures the communication and computation overhead constraints as well as the lack of a central point to install the IDS. The IDS is distributed on some nodes which are powerful in terms of memory, computation and the degree of connectivity. Our IDS uses an analytical approach for detecting Wormhole attack. We simulate wireless mesh NANs in OPNET Modeler and for the first time, we integrate our analytical model in Maple from MapleSoft with our OPNET simulation model.

scholarly journals ARIES: A Novel Multivariate Intrusion Detection System for Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5305
Author(s):  
Panagiotis Radoglou Grammatikis ◽  
Panagiotis Sarigiannidis ◽  
Georgios Efstathopoulos ◽  
Emmanouil Panaousis
Keyword(s):  
Intrusion Detection ◽  
Smart Grid ◽  
Intrusion Detection System ◽  
Network Flow ◽  
Network Flows ◽  
Detection System ◽  
Random Noise ◽  
Denial Of Service ◽  
The Third ◽  
Operational Data

The advent of the Smart Grid (SG) raises severe cybersecurity risks that can lead to devastating consequences. In this paper, we present a novel anomaly-based Intrusion Detection System (IDS), called ARIES (smArt gRid Intrusion dEtection System), which is capable of protecting efficiently SG communications. ARIES combines three detection layers that are devoted to recognising possible cyberattacks and anomalies against (a) network flows, (b) Modbus/Transmission Control Protocol (TCP) packets and (c) operational data. Each detection layer relies on a Machine Learning (ML) model trained using data originating from a power plant. In particular, the first layer (network flow-based detection) performs a supervised multiclass classification, recognising Denial of Service (DoS), brute force attacks, port scanning attacks and bots. The second layer (packet-based detection) detects possible anomalies related to the Modbus packets, while the third layer (operational data based detection) monitors and identifies anomalies upon operational data (i.e., time series electricity measurements). By emphasising on the third layer, the ARIES Generative Adversarial Network (ARIES GAN) with novel error minimisation functions was developed, considering mainly the reconstruction difference. Moreover, a novel reformed conditional input was suggested, consisting of random noise and the signal features at any given time instance. Based on the evaluation analysis, the proposed GAN network overcomes the efficacy of conventional ML methods in terms of Accuracy and the F1 score.

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