[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Tusher et al., 2024 - Google Patents

A comparative analysis of different transmission line fault detectors and classifiers during normal conditions and cyber‐attacks

Tusher et al., 2024

View PDF @Full View
Document ID
15447872324559221274
Author
Tusher A
Rahman M
Islam M
Hossain M
Publication year
Publication venue
The Journal of Engineering

External Links

Snippet

Transmission lines, the core part of the transmission and distribution system in the smart grid, require effective, efficient, and reliable protective measures against faults to avoid severe damage to physical infrastructure and financial losses. Due to their growing …
Continue reading at ietresearch.onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1416—Event detection, e.g. attack signature detection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1425—Traffic logging, e.g. anomaly detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints

Similar Documents

Publication Publication Date Title
Sayghe et al. Survey of machine learning methods for detecting false data injection attacks in power systems
Khan et al. Intelligent anomaly identification in cyber-physical inverter-based systems
Mohammadpourfard et al. A statistical unsupervised method against false data injection attacks: A visualization-based approach
Wang et al. A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems
Pavlovski et al. Hierarchical convolutional neural networks for event classification on PMU measurements
Deng et al. Deep learning model to detect various synchrophasor data anomalies
Ruan et al. Deep learning for cybersecurity in smart grids: Review and perspectives
Elsisi et al. Design of reliable IoT systems with deep learning to support resilient demand side management in smart grids against adversarial attacks
Dou et al. Pc 2 a: predicting collective contextual anomalies via lstm with deep generative model
Duan et al. Deep‐learning‐based power distribution network switch action identification leveraging dynamic features of distributed energy resources
Berghout et al. EL-NAHL: Exploring labels autoencoding in augmented hidden layers of feedforward neural networks for cybersecurity in smart grids
Zhang et al. Online power system dynamic security assessment with incomplete PMU measurements: A robust white‐box model
Singh et al. Statistical machine learning defensive mechanism against cyber intrusion in smart grid cyber-physical network
Mukherjee Detection of data-driven blind cyber-attacks on smart grid: A deep learning approach
Hasnat et al. Detecting and locating cyber and physical stresses in smart grids using the k‐nearest neighbour analysis of instantaneous correlation of states
Shitharth et al. Integrated probabilistic relevancy classification (prc) scheme for intrusion detection in scada network
D'Angelo et al. A stacked autoencoder‐based convolutional and recurrent deep neural network for detecting cyberattacks in interconnected power control systems
Shitharth et al. A new probabilistic relevancy classification (PRC) based intrusion detection system (IDS) for SCADA network
Wang et al. Deep learning enhanced situation awareness for high renewable‐penetrated power systems with multiple data corruptions
Kummerow et al. Cyber-physical data stream assessment incorporating Digital Twins in future power systems
Lawal et al. Data-driven learning-based classification model for mitigating false data injection attacks on dynamic line rating systems
Ankitdeshpandey et al. Development of intrusion detection system using deep learning for classifying attacks in power systems
Terzi et al. Smart grid security evaluation with a big data use case
Tusher et al. Adversarial training-based robust lifetime prediction system for power transformers
Tusher et al. A comparative analysis of different transmission line fault detectors and classifiers during normal conditions and cyber‐attacks