A Bagging MLP-based Autoencoder for Detection of False Data Injection Attack in Smart Grid

Paul, Shuva; Kundu, Ripan Kumar

doi:10.1109/isgt50606.2022.9817480

Cited by 4 publications

(1 citation statement)

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“…The main advantage of MLP is its ability to model complex nonlinear relationships in data. The limitation is the need for a large amount of data and the risk of overfitting, as well as a long training time compared to other methods [16,17].…”

Section: Overview Of Approaches and Methodsmentioning

confidence: 99%

Application of Heterogeneous Ensembles in Problems of Computer System State Identification

Hornostal,

Gavrylenko

2023

A.I.S.

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The object of the study is the process of identifying anomalies in the operation of a computer system (CS). The subject of the study is ensemble methods for identifying the state of the CS. The goal of the study is to improve the performance of ensemble classifiers based on heterogeneous models. Methods used: machine learning methods, homogeneous and heterogeneous ensemble classifiers, Pasting and Bootstrapping technologies. Results obtained: a comparative analysis of the use of homogeneous and heterogeneous bagging ensembles in data classification problems was carried out. The effectiveness of various approaches to the selection of base ensemble classifiers has been studied. A method for identifying the state of a computer system, based on the heterogeneous bagging ensemble was proposed. Experimental studies made it possible to confirm the main theoretical assumptions, as well as evaluate the efficiency of the constructed heterogeneous ensembles. Conclusions. Based on the results of the study, the method for constructing a heterogeneous bagging ensemble classifier, which differs from known methods in the procedure for selecting base models was proposed. It made possible to increase the classification accuracy. Further development of this research could include the creating and integration of dissimilarity metrics as well as other quantitative metrics for a more accurate and balanced base model selection procedure, which would further improve the performance of the computer system state classifier.

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Section: Overview Of Approaches and Methodsmentioning

confidence: 99%