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False Data Injection Attacks Detection in Power System Using Machine Learning Method
2018
Journal of Computer and Communications
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In this paper, we use four outlier detection methods, namely one-Class SVM, Robust covariance, Isolation forest and Local outlier factor method from machine learning area in IEEE14 simulation platform ...
State estimation in power system is the estimation of the current system state, to provide data support for the EMS (Energy Management System) to do the op-How to cite this paper: ...
Methodology: Outlier Detection Methods for False Data Injection Detection Novelty and outlier detection methods are effective methods for anomaly detec-
One-Class SVM for False Data Injection Detection ...
doi:10.4236/jcc.2018.611025
fatcat:p22fgrfmwffqlnm5aicqrs55vm
Anomaly Detection Based on Convex Analysis: A Survey
2022
Frontiers in Physics
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With the remarkable progress made in the techniques of big data and machine learning, CA-based anomaly detection holds great promise for more expeditious, accurate and intelligent detection capacities. ...
As a crucial technique for identifying irregular samples or outlier patterns, anomaly detection has broad applications in many fields. ...
Among the support vector domain methods, the support vector machine (SVM) [97] is a mainstream two-class classification method for fields such as text detection, human body recognition, and freight transportation ...
doi:10.3389/fphy.2022.873848
fatcat:ooxtghts5ffoxmu2qx743ij3eq
Data analytics for performance evaluation under uncertainties applied to an industrial refrigeration plant
2019
IEEE Access
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ACKNOWLEDGMENT The authors would like to thank the support of Corporación Alimentaria Guissona S.A. for providing access to their refrigeration plant dataset and their recommendations. ...
in comparison with other classical techniques such as One-Class Support Vector Machine (O-C SVM) [25] , which hyperparameters are unique regarding the number of the variables analyzed. ...
A performing approach is to set the bandwidths through the least squares cross-validation. ...
doi:10.1109/access.2019.2917079
fatcat:doeiivgocjddtg6ob3dv7d3cqq
A Comprehensive survey of Machine Learning for Intrusion Detection
2019
International Journal of Research in Advent Technology
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This paper presents different methods used in IDS for protecting computers and networks for over a decade. This study analyzes different machine learning methods in IDS. ...
Intrusion detection System (IDS) is one of the major research problems in network security. IDSs are developed to detect known and unknown attacks of both Computer and computer networks. ...
Available online at www.ijrat.org training labeled data, in which each training data contains a pair of an input vector and class label. ...
doi:10.32622/ijrat.72201941
fatcat:iwf7uquktjarvbg7h7sccstshi
A Pattern Recognition Approach for Peak Prediction of Electrical Consumption
[chapter]
2014
IFIP Advances in Information and Communication Technology
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Demand peaks in electrical power consumptions pose serious challenges for energy companies as these are typically unforeseen and require the net to support abnormally high consumption levels. ...
Furthermore, we introduce a novel Learning Automata based approaches for selecting the optimal prediction model from a pool of models in an online fashion. ...
Outlier detection Much work has been carried out on outlier detection [12, 3] . This research field resembles peak prediction in many ways. ...
doi:10.1007/978-3-662-44654-6_26
fatcat:sheal2mt7ne5pivb6nlp5fs37e
Database Systems for the Smart Grid
[chapter]
2013
Green Energy and Technology
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In this chapter, two aspects of database systems, namely database management and data mining, for the smart grid are covered. ...
The uses of database management and data mining for the electrical power grid comprising of the interrelated subsystems of power generation, transmission, distribution, and utilization are discussed. ...
research project titled "Data Mining for Smart Grids". ...
doi:10.1007/978-1-4471-5210-1_7
fatcat:ct7bfrxni5gkpk2oj67o4ahxsy
Towards area classification for large-scale fingerprint-based system
2016
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '16
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We first study leveraging the one-class classification to conduct inside/outside-region detection given only the inside fingerprints. ...
In spacious and multi-area buildings, fingerprint-based localization often suffers from expensive location search. ...
This work was supported, in part, by Hong Kong Research Grant Council (RGC) General Research Fund (610713). ...
doi:10.1145/2971648.2971689
dblp:conf/huc/HeTC16
fatcat:eu7yeevchfcovofki6dtgq67ae
A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systems
[article]
2020
arXiv
pre-print
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Among them, False Data Injection Attacks (FDIAs) is a class of cyberattacks against power grid monitoring systems. ...
In order to overcome the limitations of traditional residual-based BDD approaches, data-driven solutions based on machine learning algorithms have been widely adopted for detecting malicious manipulation ...
W = diag σ 2 1 , σ 2 2 , · · · , σ 2 2n (4) The Weighted Least Square (WLS) technique is one of the most commonly used methods for SE [10] . ...
arXiv:2008.06926v1
fatcat:grxa2q5vords3ew2a6kuzrw3im
Data Mining Methods: A Review
2022
International Journal of Computer Applications
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[Online].
Classifiers, K-Nearest Neighbor, Support Vector Machine etc. ...
for Machine Learning.
[35] T. Soni Madhulatha. (April 2012). ―An overview on
[21]Mean Squared Error (MSE). [Online]. ...
doi:10.5120/ijca2022921884
fatcat:336vrehj6bgm5dfiqavmhj35mi
Survey of machine learning methods for detecting false data injection attacks in power systems
2020
IET Smart Grid
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Among them, false data injection attacks (FDIAs) are a class of cyber-attacks against power grid monitoring systems. ...
This study provides a comprehensive review of the most up-to-date machine learning methods for detecting FDIAs against power system SE algorithms. ...
represents a support vector, as shown in Fig. 3 . ...
doi:10.1049/iet-stg.2020.0015
fatcat:kzfjgzhmybgntijdpszkhk4p7i
Online power quality disturbance detection by support vector machine in smart meter
2018
Journal of Modern Power Systems and Clean Energy
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Addressing this issue, in this study, we propose segregation of the power disturbance from regular values using one-class support vector machine (OCSVM). ...
Power quality assessment is an important performance measurement in smart grids. ...
Acknowledgements This research was supported in part through U.S. National Science Foundation (No. 1553494). Imtiaz PARVEZ and Maryamossadat AGHILI contributed equally to this work. ...
doi:10.1007/s40565-018-0488-z
fatcat:o6pa7sqrfrgzbngchutbm7ha5u
A Survey of Outlier Detection Techniques in IoT: Review and Classification
2022
Journal of Sensor and Actuator Networks
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In this context, one of the main issues requiring more research and adapted solutions is the outlier detection problem. ...
In this paper, we propose a comprehensive literature review of recent outlier detection techniques used in the IoTs context. ...
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/jsan11010004
fatcat:5b2h43grsrewxdtag3ezgtjjle
Outlier Detection Strategies for WSNs: A Survey
2021
Journal of King Saud University: Computer and Information Sciences
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Thus, detecting outliers in WSNs using data-driven approaches becomes a novel technique among the Machine Learning (ML) communities. ...
capability and power sources to ensure data quality. ...
The second one a quarter-sphere support-vector-machine, which makes the spheres possible size detain normal data vectors in the advanced dimensional margin for all sensor nodes. ...
doi:10.1016/j.jksuci.2021.02.012
fatcat:rpgswasszzbgdbkziskhqrqjam
Program
2009
2009 International Joint Conference on Neural Networks
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Subspace Based Least Squares Support Vector Machines for Pattern Classification Takuya Kitamura, Shigeo Abe and Kazuhiro Fukui P230 An Integrated Approach of Particle Swarm Optimization and Support Vector ...
Neto P1321 Discovering Novelty in Spatio/Temporal Data Using One-Class Support Vector Machines Koen Smets, Brigitte Verdonk and Elsa Jordaan P1322 Combination of Generative Models and SVM Based Classifier ...
doi:10.1109/ijcnn.2009.5178575
fatcat:kxaceopferd23ps5uyrn3m7xjy
Machine learning for internet of things data analysis: a survey
2018
Digital Communications and Networks
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A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration. ...
The potential and challenges of machine learning for IoT data analytics will also be discussed. ...
Ajit Joakar for their comments on the draft of the paper. We also thank Dr. Alireza Ahrabian and Utkarshani Jiamini for reviewing our paper. ...
doi:10.1016/j.dcan.2017.10.002
fatcat:blmg3fed4rajrnsksixvg2afty
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