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About Explainable Machine Learning Models for Anomaly Detection in Cyber-Physical Systems: book chapter. / Chernyshov, Yury.
Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23): book. ed. / S. Kovalev; A. Sukhanov; I. Kotenko. Springer Cham, 2023. p. 106-114 Chapter 11 (Lecture Notes in Networks and Systems; Vol. 777).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Chernyshov, Y 2023, About Explainable Machine Learning Models for Anomaly Detection in Cyber-Physical Systems: book chapter. in S Kovalev, A Sukhanov & I Kotenko (eds), Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23): book., Chapter 11, Lecture Notes in Networks and Systems, vol. 777, Springer Cham, pp. 106-114. https://doi.org/10.1007/978-3-031-43792-2_11

APA

Chernyshov, Y. (2023). About Explainable Machine Learning Models for Anomaly Detection in Cyber-Physical Systems: book chapter. In S. Kovalev, A. Sukhanov, & I. Kotenko (Eds.), Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23): book (pp. 106-114). [Chapter 11] (Lecture Notes in Networks and Systems; Vol. 777). Springer Cham. https://doi.org/10.1007/978-3-031-43792-2_11

Vancouver

Chernyshov Y. About Explainable Machine Learning Models for Anomaly Detection in Cyber-Physical Systems: book chapter. In Kovalev S, Sukhanov A, Kotenko I, editors, Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23): book. Springer Cham. 2023. p. 106-114. Chapter 11. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-43792-2_11

Author

Chernyshov, Yury. / About Explainable Machine Learning Models for Anomaly Detection in Cyber-Physical Systems : book chapter. Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23): book. editor / S. Kovalev ; A. Sukhanov ; I. Kotenko. Springer Cham, 2023. pp. 106-114 (Lecture Notes in Networks and Systems).

BibTeX

@inproceedings{faba273b6886419690e0a4ff9e85a77c,
title = "About Explainable Machine Learning Models for Anomaly Detection in Cyber-Physical Systems: book chapter",
abstract = "The paper considers methods for interpreting anomalies in time-ordered data of different structure with the use of special methods for analyzing the behavior of neural networks. The use of reconstruction-based anomaly detection methods makes it possible to detect anomalous patterns in the data, but it does not make it possible to conclude which feature had a decisive influence on the result of the model inference. The paper provides an overview of research in this direction, including the description of well-known methods for interpreting deep learning models and their modifications for analyzing the dataset of a cyber-physical system. Those methods are used for anomaly detection and interpretation for cyber physical system.",
author = "Yury Chernyshov",
year = "2023",
month = sep,
day = "18",
doi = "10.1007/978-3-031-43792-2_11",
language = "English",
isbn = "978-3-031-43791-5",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Cham",
pages = "106--114",
editor = "S. Kovalev and A. Sukhanov and I. Kotenko",
booktitle = "Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI{\textquoteright}23)",
address = "United Kingdom",

}

RIS

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AU - Chernyshov, Yury

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N2 - The paper considers methods for interpreting anomalies in time-ordered data of different structure with the use of special methods for analyzing the behavior of neural networks. The use of reconstruction-based anomaly detection methods makes it possible to detect anomalous patterns in the data, but it does not make it possible to conclude which feature had a decisive influence on the result of the model inference. The paper provides an overview of research in this direction, including the description of well-known methods for interpreting deep learning models and their modifications for analyzing the dataset of a cyber-physical system. Those methods are used for anomaly detection and interpretation for cyber physical system.

AB - The paper considers methods for interpreting anomalies in time-ordered data of different structure with the use of special methods for analyzing the behavior of neural networks. The use of reconstruction-based anomaly detection methods makes it possible to detect anomalous patterns in the data, but it does not make it possible to conclude which feature had a decisive influence on the result of the model inference. The paper provides an overview of research in this direction, including the description of well-known methods for interpreting deep learning models and their modifications for analyzing the dataset of a cyber-physical system. Those methods are used for anomaly detection and interpretation for cyber physical system.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85174437912

U2 - 10.1007/978-3-031-43792-2_11

DO - 10.1007/978-3-031-43792-2_11

M3 - Conference contribution

SN - 978-3-031-43791-5

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EP - 114

BT - Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23)

A2 - Kovalev, S.

A2 - Sukhanov, A.

A2 - Kotenko, I.

PB - Springer Cham

ER -

ID: 46905519