A Subspace Method for Time Series Anomaly Detection in Cyber-Physical Systems

被引：0

作者：

Vides, Fredy ^{[1
,2
]}

Segura, Esteban ^{[3
]}

Vargas-Aguero, Carlos ^{[4
]}

机构：

[1] Univ Nacl Autonoma Honduras, Sci Comp Innovat Ctr, Tegucigalpa, Honduras

[2] Dept Anal & Informat, Natl Banking & Insurance Commiss, Tegucigalpa, Honduras

[3] Univ Costa Rica, Escuela Matemat CIMPA, San Jose, Costa Rica

[4] Univ Costa Rica, Escuela Fis, San Jose, Costa Rica

来源：

IFAC PAPERSONLINE | 2022年 / 55卷 / 41期

关键词：

Anomaly detection; Hankel matrix; time series analysis; sensors; signals;

D O I：

10.1016/j.ifacol.2023.01.103

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Time series anomaly detection is an important process for system monitoring and model switching, among other applications in cyber-physical systems. In this document we present a fast subspace method for time series anomaly detection, with a relatively low computational cost, that has been designed for anomaly detection in real sensor signals corresponding to dynamical systems. We also present some general results corresponding to the theoretical foundations of our method, together with a prototypical algorithm for time series anomaly detection. Some numerical examples corresponding to applications of the prototypical algorithm are presented, and some computational tools based on the theory and algorithms presented in this paper, are provided. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

引用

页码：58 / 63

页数：6

共 50 条

[11] Cyber-Physical Anomaly Detection for ICS
Wuestrich, Lars
Schroeder, Lukas
Pahl, Marc-Oliver
2021 IFIP/IEEE INTERNATIONAL SYMPOSIUM ON INTEGRATED NETWORK MANAGEMENT (IM 2021), 2021, : 950 - 955
[12] Anomaly Detection and Productivity Analysis for Cyber-Physical Systems in Manufacturing
Saez, Miguel
Maturana, Francisco
Barton, Kira
Tilbury, Dawn
2017 13TH IEEE CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE), 2017, : 23 - 29
[13] Anomaly Detection in Cyber-Physical Systems: A Formal Methods Approach
Jones, Austin
Kong, Zhaodan
Belta, Calin
2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2014, : 848 - 853
[14] Multipath neural networks for anomaly detection in cyber-physical systems
Raphaël M. J. I. Larsen
Marc-Oliver Pahl
Gouenou Coatrieux
Annals of Telecommunications, 2023, 78 : 149 - 167
[15] Anomaly Diagnosis in Cyber-Physical Systems
Cook, Marco
Paterson, Cory
Marnerides, Angelos K.
Pezaros, Dimitrios
IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022), 2022, : 5445 - 5450
[16] Using Ensemble Learning for Anomaly Detection in Cyber-Physical Systems
Jeffrey, Nicholas
Tan, Qing
Villar, Jose R.
ELECTRONICS, 2024, 13 (07)
[17] illiad: InteLLigent Invariant and Anomaly Detection in Cyber-Physical Systems
Muralidhar, Nikhil
Wang, Chen
Self, Nathan
Momtazpour, Marjan
Nakayama, Kiyoshi
Sharma, Ratnesh
Ramakrishnan, Naren
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2018, 9 (03)
[18] Multipath neural networks for anomaly detection in cyber-physical systems
Larsen, Raphael M. J., I
Pahl, Marc-Oliver
Coatrieux, Gouenou
ANNALS OF TELECOMMUNICATIONS, 2023, 78 (3-4) : 149 - 167
[19] Anomaly detection method based on penalty least squares algorithm and time window entropy for Cyber-Physical Systems
Zhang, Jing
Yuan, Yige
Zhang, Jiahong
Yang, Yang
Xie, Wenjin
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2023, 35 (10)
[20] An Anomaly Detection Method of Time Series Data for Cyber-Physical Integrated Energy System Based on Time-Frequency Feature Prediction
Chen, Jinyi
Zhou, Suyang
Qiu, Yue
Xu, Boya
ENERGIES, 2022, 15 (15)

← 1 2 3 4 5 →