Abstract
No public abstract is available from the verified source records.
Nahal Azadi, Emad Efatinasab, Mirco Rampazzo
Abstract
Authors
Institutions
Provenance
crossref
Confidence 100%
openalex
Confidence 95%
datacite
Confidence 0%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Fortifying smart grid stability: defending against adversarial attacks and measurement anomalies
2025
Dynamic graph-based anomaly detection in the electrical grid
10.1109/tpwrs.2021.3132852 · 2022
A survey on the detection algorithms for false data injection attacks in smart grids
10.1109/tsg.2019.2949998 · 2020
From static to dynamic anomaly detection with application to power system cyber security
10.1109/tpwrs.2019.2943304 · 2020
Developing a hybrid intrusion detection system using data mining for power systems
10.1109/tsg.2015.2409775 · 2015
Classification of disturbances and cyber-attacks in power systems using heterogeneous time-synchronized data
10.1109/tii.2015.2420951 · 2015
Detection of integrity attacks in cyber-physical critical infrastructures using ensemble modeling
10.1109/tii.2014.2367322 · 2015
Unresolved referenced work
Kept as external metadata until matched
Cyber-attack detection for industrial control system monitoring with support vector machine based on communication profile
2017
Machine learning for power system disturbance and cyber-attack discrimination
2014
Multivariate physics-informed convolutional autoencoder for anomaly detection in power distribution systems with widespread deployment of distributed energy resources
2025
A false sense of security? Revisiting the state of machine learning-based industrial intrusion detection
2022
Unresolved referenced work
Kept as external metadata until matched
Which algorithm can detect unknown attacks? Comparison of supervised, unsupervised and meta-learning algorithms for intrusion detection
10.1016/j.cose.2023.103107 · 2023
Anomaly detection using LSTM-based variational autoencoder in unsupervised data in power grid
10.1109/jsyst.2023.3266554 · 2023
Real-time synchrophasor data anomaly detection and classification using isolation forest, KMeans, and LoOP
10.1109/tsg.2020.3046602 · 2021
Unsupervised anomaly detection and diagnosis in power electronic networks: informative leverage and multivariate functional clustering approaches
10.1109/tsg.2023.3325276 · 2024
Enhancing anomaly detection in distributed power systems using autoencoder-based federated learning
10.1371/journal.pone.0290337 · 2023
A3D: attention-based auto-encoder anomaly detector for false data injection attacks
10.1016/j.epsr.2020.106795 · 2020
An unsupervised adversarial autoencoder for cyber attack detection in power distribution grids
10.1016/j.epsr.2024.110407 · 2024
Exploiting autoencoder-based anomaly detection to enhance cybersecurity in power grids
10.3390/fi16060184 · 2024
Cyber-physical anomaly detection for inverter-based microgrid using autoencoder neural network
10.1016/j.apenergy.2023.122283 · 2024
Anomaly detection for power system forecasting under data corruption based on variational auto-encoder
2019
Anomaly detection using invariant rules in industrial control systems
10.1016/j.conengprac.2024.106164 · 2025
Anomaly detection in industrial control systems using logical analysis of data
10.1016/j.cose.2020.101935 · 2020
Collaborative defense against data injection attack in IEC61850 based smart substations
2016
A case study on implementing false data injection attacks against nonlinear state estimation
2016
Unresolved referenced work
Kept as external metadata until matched
History of Industrial Control System Cyber Incidents
2018
A physical overlay framework for insider threat mitigation of power system devices
2014
Optimal defensive strategy for power distribution systems against relay setting attacks
10.1109/tpwrd.2022.3230946 · 2023
Investigating man-in-the-middle-based false data injection in a smart grid laboratory environment
2021
A survey on industrial control system testbeds and datasets for security research
10.1109/comst.2021.3094360 · 2021
Classification of intrusion cyber-attacks in smart power grids using deep ensemble learning with metaheuristic-based optimization
10.1111/exsy.13556 · 2025
Decentralized cybersecurity in smart grids: leveraging location-fedavg for rapid threat detection and adaptive resilience
2026
Deep one-class classification
2018
Gaussian mixture models
2009
Angle-based outlier detection in high-dimensional data
2008
Efficient algorithms for mining outliers from large data sets
2000
Fast outlier detection in high dimensional spaces
2002
A design-driven machine learning approach for invariant mining in a smart grid
10.1049/cps2.70043 · doi-reference
Discovering cluster-based local outliers
10.1016/s0167-8655(03)00003-5 · doi-reference
Classification of intrusion cyber-attacks in smart power grids using deep ensemble learning with metaheuristic-based optimization
10.1111/exsy.13556 · doi-reference
A survey on industrial control system testbeds and datasets for security research
10.1109/comst.2021.3094360 · doi-reference
Optimal defensive strategy for power distribution systems against relay setting attacks
10.1109/tpwrd.2022.3230946 · doi-reference
Anomaly detection in industrial control systems using logical analysis of data
10.1016/j.cose.2020.101935 · doi-reference
Anomaly detection using invariant rules in industrial control systems
10.1016/j.conengprac.2024.106164 · doi-reference
Cyber-physical anomaly detection for inverter-based microgrid using autoencoder neural network
10.1016/j.apenergy.2023.122283 · doi-reference
Exploiting autoencoder-based anomaly detection to enhance cybersecurity in power grids
10.3390/fi16060184 · doi-reference
An unsupervised adversarial autoencoder for cyber attack detection in power distribution grids
10.1016/j.epsr.2024.110407 · doi-reference
A3D: attention-based auto-encoder anomaly detector for false data injection attacks
10.1016/j.epsr.2020.106795 · doi-reference
Enhancing anomaly detection in distributed power systems using autoencoder-based federated learning
10.1371/journal.pone.0290337 · doi-reference
Unsupervised anomaly detection and diagnosis in power electronic networks: informative leverage and multivariate functional clustering approaches
10.1109/tsg.2023.3325276 · doi-reference
Real-time synchrophasor data anomaly detection and classification using isolation forest, KMeans, and LoOP
10.1109/tsg.2020.3046602 · doi-reference
Anomaly detection using LSTM-based variational autoencoder in unsupervised data in power grid
10.1109/jsyst.2023.3266554 · doi-reference
Which algorithm can detect unknown attacks? Comparison of supervised, unsupervised and meta-learning algorithms for intrusion detection
10.1016/j.cose.2023.103107 · doi-reference
Detection of integrity attacks in cyber-physical critical infrastructures using ensemble modeling
10.1109/tii.2014.2367322 · doi-reference
Classification of disturbances and cyber-attacks in power systems using heterogeneous time-synchronized data
10.1109/tii.2015.2420951 · doi-reference
Developing a hybrid intrusion detection system using data mining for power systems
10.1109/tsg.2015.2409775 · doi-reference
From static to dynamic anomaly detection with application to power system cyber security
10.1109/tpwrs.2019.2943304 · doi-reference
A survey on the detection algorithms for false data injection attacks in smart grids
10.1109/tsg.2019.2949998 · doi-reference
Dynamic graph-based anomaly detection in the electrical grid
10.1109/tpwrs.2021.3132852 · doi-reference