A New Data Model for Behavioral Based Anomaly Detection in IoT Device Monitoring

Marcin Michalak; Piotr Biczyk; Błażej Adamczyk; Maksym Brzȩczek; Marek Hermansa; Iwona Kostorz; Łukasz Wawrowski; Michał Czerwiński

doi:10.1007/978-3-031-50959-9_41

A New Data Model for Behavioral Based Anomaly Detection in IoT Device Monitoring

Marcin Michalak
, Piotr Biczyk
, Błażej Adamczyk
, Maksym Brzȩczek
, Marek Hermansa
, Iwona Kostorz
, Łukasz Wawrowski
, Michał Czerwiński

Institute of Innovative Technologies EMAG
QED Software sp. z o.o.
EFIGO sp. z o.o.
University of Warsaw

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

1 Citation (Scopus)

Abstract

The paper presents the new model of data for Internet of Things (IoT) devices monitoring and anomaly detection. The model bases mostly on behavioral description of current state of device, however it contains also some additional information. Raw input data, coming from the external simulation software, are aggregated on two levels of detail: raw variable values preprocessing and time-based aggregation. It was shown, that a sample data following this model, data that contains anomalies, can be analyzed with standard anomaly detection methods and results of this application are very satisfactory. The data used in the paper are also publicly available.

Original language	English
Title of host publication	Rough Sets - International Joint Conference, IJCRS 2023, Proceedings
Editors	Andrea Campagner, Oliver Urs Lenz, Shuyin Xia, Dominik Ślęzak, Jarosław Wąs, JingTao Yao
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	599-611
Number of pages	13
ISBN (Print)	9783031509582
DOIs	https://doi.org/10.1007/978-3-031-50959-9_41
Publication status	Published - 2023
Event	International Joint Conference on Rough Sets, IJCRS 2023 - Krakow, Poland Duration: 5 Oct 2023 → 8 Oct 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14481 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Joint Conference on Rough Sets, IJCRS 2023
Country/Territory	Poland
City	Krakow
Period	5/10/23 → 8/10/23

Keywords

Anomaly detection
Behavioral analysis
Internet of Things

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-50959-9_41

Cite this

Michalak, M., Biczyk, P., Adamczyk, B., Brzȩczek, M., Hermansa, M., Kostorz, I., Wawrowski, Ł., & Czerwiński, M. (2023). A New Data Model for Behavioral Based Anomaly Detection in IoT Device Monitoring. In A. Campagner, O. Urs Lenz, S. Xia, D. Ślęzak, J. Wąs, & J. Yao (Eds.), Rough Sets - International Joint Conference, IJCRS 2023, Proceedings (pp. 599-611). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14481 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-50959-9_41

Michalak, Marcin ; Biczyk, Piotr ; Adamczyk, Błażej et al. / A New Data Model for Behavioral Based Anomaly Detection in IoT Device Monitoring. Rough Sets - International Joint Conference, IJCRS 2023, Proceedings. editor / Andrea Campagner ; Oliver Urs Lenz ; Shuyin Xia ; Dominik Ślęzak ; Jarosław Wąs ; JingTao Yao. Springer Science and Business Media Deutschland GmbH, 2023. pp. 599-611 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The paper presents the new model of data for Internet of Things (IoT) devices monitoring and anomaly detection. The model bases mostly on behavioral description of current state of device, however it contains also some additional information. Raw input data, coming from the external simulation software, are aggregated on two levels of detail: raw variable values preprocessing and time-based aggregation. It was shown, that a sample data following this model, data that contains anomalies, can be analyzed with standard anomaly detection methods and results of this application are very satisfactory. The data used in the paper are also publicly available.",

keywords = "Anomaly detection, Behavioral analysis, Internet of Things",

author = "Marcin Michalak and Piotr Biczyk and B{\l}a{\.z}ej Adamczyk and Maksym Brzȩczek and Marek Hermansa and Iwona Kostorz and {\L}ukasz Wawrowski and Micha{\l} Czerwi{\'n}ski",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; International Joint Conference on Rough Sets, IJCRS 2023 ; Conference date: 05-10-2023 Through 08-10-2023",

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editor = "Andrea Campagner and \{Urs Lenz\}, Oliver and Shuyin Xia and Dominik {\'S}l{\c e}zak and Jaros{\l}aw W{\c a}s and JingTao Yao",

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Michalak, M , Biczyk, P , Adamczyk, B, Brzȩczek, M, Hermansa, M, Kostorz, I, Wawrowski, Ł & Czerwiński, M 2023, A New Data Model for Behavioral Based Anomaly Detection in IoT Device Monitoring. in A Campagner, O Urs Lenz, S Xia, D Ślęzak, J Wąs & J Yao (eds), Rough Sets - International Joint Conference, IJCRS 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14481 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 599-611, International Joint Conference on Rough Sets, IJCRS 2023, Krakow, Poland, 5/10/23. https://doi.org/10.1007/978-3-031-50959-9_41

A New Data Model for Behavioral Based Anomaly Detection in IoT Device Monitoring. / Michalak, Marcin ; Biczyk, Piotr ; Adamczyk, Błażej et al.
Rough Sets - International Joint Conference, IJCRS 2023, Proceedings. ed. / Andrea Campagner; Oliver Urs Lenz; Shuyin Xia; Dominik Ślęzak; Jarosław Wąs; JingTao Yao. Springer Science and Business Media Deutschland GmbH, 2023. p. 599-611 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14481 LNAI).

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

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T1 - A New Data Model for Behavioral Based Anomaly Detection in IoT Device Monitoring

AU - Michalak, Marcin

AU - Biczyk, Piotr

AU - Adamczyk, Błażej

AU - Brzȩczek, Maksym

AU - Hermansa, Marek

AU - Kostorz, Iwona

AU - Wawrowski, Łukasz

AU - Czerwiński, Michał

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

PY - 2023

Y1 - 2023

N2 - The paper presents the new model of data for Internet of Things (IoT) devices monitoring and anomaly detection. The model bases mostly on behavioral description of current state of device, however it contains also some additional information. Raw input data, coming from the external simulation software, are aggregated on two levels of detail: raw variable values preprocessing and time-based aggregation. It was shown, that a sample data following this model, data that contains anomalies, can be analyzed with standard anomaly detection methods and results of this application are very satisfactory. The data used in the paper are also publicly available.

AB - The paper presents the new model of data for Internet of Things (IoT) devices monitoring and anomaly detection. The model bases mostly on behavioral description of current state of device, however it contains also some additional information. Raw input data, coming from the external simulation software, are aggregated on two levels of detail: raw variable values preprocessing and time-based aggregation. It was shown, that a sample data following this model, data that contains anomalies, can be analyzed with standard anomaly detection methods and results of this application are very satisfactory. The data used in the paper are also publicly available.

KW - Anomaly detection

KW - Behavioral analysis

KW - Internet of Things

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DO - 10.1007/978-3-031-50959-9_41

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AN - SCOPUS:85181980985

SN - 9783031509582

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 599

EP - 611

BT - Rough Sets - International Joint Conference, IJCRS 2023, Proceedings

A2 - Campagner, Andrea

A2 - Urs Lenz, Oliver

A2 - Xia, Shuyin

A2 - Ślęzak, Dominik

A2 - Wąs, Jarosław

A2 - Yao, JingTao

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Joint Conference on Rough Sets, IJCRS 2023

Y2 - 5 October 2023 through 8 October 2023

ER -

Michalak M , Biczyk P , Adamczyk B, Brzȩczek M, Hermansa M, Kostorz I et al. A New Data Model for Behavioral Based Anomaly Detection in IoT Device Monitoring. In Campagner A, Urs Lenz O, Xia S, Ślęzak D, Wąs J, Yao J, editors, Rough Sets - International Joint Conference, IJCRS 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 599-611. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-50959-9_41