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MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection
Intrusion detection systems examine the computer or network for potential security vulnerabilities. Time series data is real-valued. The nature of the data influences the type of anomaly detection. As a result, network anomalies are operations that deviate from the norm. These anomalies can cause a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534607/ https://www.ncbi.nlm.nih.gov/pubmed/36211006 http://dx.doi.org/10.1155/2022/4728063 |
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author | Reyana, A. Kautish, Sandeep Yahia, I. S. Mohamed, Ali Wagdy |
author_facet | Reyana, A. Kautish, Sandeep Yahia, I. S. Mohamed, Ali Wagdy |
author_sort | Reyana, A. |
collection | PubMed |
description | Intrusion detection systems examine the computer or network for potential security vulnerabilities. Time series data is real-valued. The nature of the data influences the type of anomaly detection. As a result, network anomalies are operations that deviate from the norm. These anomalies can cause a wide range of device malfunctions, overloads, and network intrusions. As a result of this, the network's normal operation and services will be disrupted. The paper proposes a new multi-variant time series-based encoder-decoder system for dealing with anomalies in time series data with multiple variables. As a result, to update network weights via backpropagation, a radical loss function is defined. Anomaly scores are used to evaluate performance. The anomaly score, according to the findings, is more stable and traceable, with fewer false positives and negatives. The proposed system's efficiency is compared to three existing approaches: Multiscaling Convolutional Recurrent Encoder-Decoder, Autoregressive Moving Average, and Long Short Term Medium-Encoder-Decoder. The results show that the proposed technique has the highest precision of 1 for a noise level of 0.2. Thus, it demonstrates greater precision for noise factors of 0.25, 0.3, 0.35, and 0.4, and its effectiveness. |
format | Online Article Text |
id | pubmed-9534607 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95346072022-10-06 MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection Reyana, A. Kautish, Sandeep Yahia, I. S. Mohamed, Ali Wagdy Comput Intell Neurosci Research Article Intrusion detection systems examine the computer or network for potential security vulnerabilities. Time series data is real-valued. The nature of the data influences the type of anomaly detection. As a result, network anomalies are operations that deviate from the norm. These anomalies can cause a wide range of device malfunctions, overloads, and network intrusions. As a result of this, the network's normal operation and services will be disrupted. The paper proposes a new multi-variant time series-based encoder-decoder system for dealing with anomalies in time series data with multiple variables. As a result, to update network weights via backpropagation, a radical loss function is defined. Anomaly scores are used to evaluate performance. The anomaly score, according to the findings, is more stable and traceable, with fewer false positives and negatives. The proposed system's efficiency is compared to three existing approaches: Multiscaling Convolutional Recurrent Encoder-Decoder, Autoregressive Moving Average, and Long Short Term Medium-Encoder-Decoder. The results show that the proposed technique has the highest precision of 1 for a noise level of 0.2. Thus, it demonstrates greater precision for noise factors of 0.25, 0.3, 0.35, and 0.4, and its effectiveness. Hindawi 2022-09-28 /pmc/articles/PMC9534607/ /pubmed/36211006 http://dx.doi.org/10.1155/2022/4728063 Text en Copyright © 2022 A. Reyana et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Reyana, A. Kautish, Sandeep Yahia, I. S. Mohamed, Ali Wagdy MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection |
title | MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection |
title_full | MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection |
title_fullStr | MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection |
title_full_unstemmed | MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection |
title_short | MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection |
title_sort | mteds: multivariant time series-based encoder-decoder system for anomaly detection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534607/ https://www.ncbi.nlm.nih.gov/pubmed/36211006 http://dx.doi.org/10.1155/2022/4728063 |
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