Anomaly Detection in Logical Sub-Views of WSNs

Robert Zakrzewski; Trevor P Martin; George Oikonomou

doi:10.1109/ISCC55528.2022.9912826

Anomaly Detection in Logical Sub-Views of WSNs

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Abstract

Wireless sensor networks are often distributed, diverse, and large making their monitoring hard. One way to tackle it is to focus on part of the system by creating logical sub-views which can be seen as proxies of the overall system operations. In this manuscript, logical sub-views consist of traffic aggregators and their topology which are monitored for anomaly. The aggregators are selected based on diversity and importance in the system and they are modelled as graphs to capture aggregation topology and data distributions. The aggregators' selection criteria, the method for comparison of partially overlapping sub-views, normal aggregation profiles acquisition, and measures of anomaly are proposed. A simulated wireless sensor network is used to acquire data at the edge and apply the method to demonstrate that focusing on system sub-views and comparing aggregation profiles facilitates anomaly detection also caused elsewhere in the system and the impact the anomaly has on aggregators.

Original language	English
Title of host publication	2022 IEEE Symposium on Computers and Communications, ISCC 2022
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	978-1-6654-9792-3
ISBN (Print)	978-1-6654-9793-0
DOIs	https://doi.org/10.1109/ISCC55528.2022.9912826
Publication status	Published - 19 Oct 2022
Event	27th IEEE Symposium on Computers and Communications - Rhodes Island, Rhodes Island, Greece Duration: 30 Jun 2022 → 3 Jul 2022 https://iscc2022.unipi.gr/

Publication series

Name	Proceedings - IEEE Symposium on Computers and Communications
Volume	2022-June
ISSN (Print)	1530-1346

Conference

Conference	27th IEEE Symposium on Computers and Communications
Abbreviated title	ISCC
Country/Territory	Greece
City	Rhodes Island
Period	30/06/22 → 3/07/22
Internet address	https://iscc2022.unipi.gr/

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Anomaly Detection
Machine Learning
Sensor Networks
Graph
Traffic Aggregation
Cyber-security

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Cite this

@inproceedings{54cbc5f34b3f475ca0e5b103fef63797,

title = "Anomaly Detection in Logical Sub-Views of WSNs",

abstract = "Wireless sensor networks are often distributed, diverse, and large making their monitoring hard. One way to tackle it is to focus on part of the system by creating logical sub-views which can be seen as proxies of the overall system operations. In this manuscript, logical sub-views consist of traffic aggregators and their topology which are monitored for anomaly. The aggregators are selected based on diversity and importance in the system and they are modelled as graphs to capture aggregation topology and data distributions. The aggregators' selection criteria, the method for comparison of partially overlapping sub-views, normal aggregation profiles acquisition, and measures of anomaly are proposed. A simulated wireless sensor network is used to acquire data at the edge and apply the method to demonstrate that focusing on system sub-views and comparing aggregation profiles facilitates anomaly detection also caused elsewhere in the system and the impact the anomaly has on aggregators.",

keywords = "Anomaly Detection, Machine Learning, Sensor Networks, Graph, Traffic Aggregation, Cyber-security",

author = "Robert Zakrzewski and Martin, \{Trevor P\} and George Oikonomou",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 27th IEEE Symposium on Computers and Communications, ISCC ; Conference date: 30-06-2022 Through 03-07-2022",

year = "2022",

month = oct,

day = "19",

doi = "10.1109/ISCC55528.2022.9912826",

language = "English",

isbn = "978-1-6654-9793-0",

series = "Proceedings - IEEE Symposium on Computers and Communications",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

booktitle = "2022 IEEE Symposium on Computers and Communications, ISCC 2022",

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url = "https://iscc2022.unipi.gr/",

}

Zakrzewski, R, Martin, TP & Oikonomou, G 2022, Anomaly Detection in Logical Sub-Views of WSNs. in 2022 IEEE Symposium on Computers and Communications, ISCC 2022. Proceedings - IEEE Symposium on Computers and Communications, vol. 2022-June, Institute of Electrical and Electronics Engineers (IEEE), 27th IEEE Symposium on Computers and Communications, Rhodes Island, Greece, 30/06/22. https://doi.org/10.1109/ISCC55528.2022.9912826

Anomaly Detection in Logical Sub-Views of WSNs. / Zakrzewski, Robert; Martin, Trevor P; Oikonomou, George.
2022 IEEE Symposium on Computers and Communications, ISCC 2022. Institute of Electrical and Electronics Engineers (IEEE), 2022. (Proceedings - IEEE Symposium on Computers and Communications; Vol. 2022-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Anomaly Detection in Logical Sub-Views of WSNs

AU - Zakrzewski, Robert

AU - Martin, Trevor P

AU - Oikonomou, George

PY - 2022/10/19

Y1 - 2022/10/19

N2 - Wireless sensor networks are often distributed, diverse, and large making their monitoring hard. One way to tackle it is to focus on part of the system by creating logical sub-views which can be seen as proxies of the overall system operations. In this manuscript, logical sub-views consist of traffic aggregators and their topology which are monitored for anomaly. The aggregators are selected based on diversity and importance in the system and they are modelled as graphs to capture aggregation topology and data distributions. The aggregators' selection criteria, the method for comparison of partially overlapping sub-views, normal aggregation profiles acquisition, and measures of anomaly are proposed. A simulated wireless sensor network is used to acquire data at the edge and apply the method to demonstrate that focusing on system sub-views and comparing aggregation profiles facilitates anomaly detection also caused elsewhere in the system and the impact the anomaly has on aggregators.

AB - Wireless sensor networks are often distributed, diverse, and large making their monitoring hard. One way to tackle it is to focus on part of the system by creating logical sub-views which can be seen as proxies of the overall system operations. In this manuscript, logical sub-views consist of traffic aggregators and their topology which are monitored for anomaly. The aggregators are selected based on diversity and importance in the system and they are modelled as graphs to capture aggregation topology and data distributions. The aggregators' selection criteria, the method for comparison of partially overlapping sub-views, normal aggregation profiles acquisition, and measures of anomaly are proposed. A simulated wireless sensor network is used to acquire data at the edge and apply the method to demonstrate that focusing on system sub-views and comparing aggregation profiles facilitates anomaly detection also caused elsewhere in the system and the impact the anomaly has on aggregators.

KW - Anomaly Detection

KW - Machine Learning

KW - Sensor Networks

KW - Graph

KW - Traffic Aggregation

KW - Cyber-security

U2 - 10.1109/ISCC55528.2022.9912826

DO - 10.1109/ISCC55528.2022.9912826

M3 - Conference Contribution (Conference Proceeding)

SN - 978-1-6654-9793-0

T3 - Proceedings - IEEE Symposium on Computers and Communications

BT - 2022 IEEE Symposium on Computers and Communications, ISCC 2022

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 27th IEEE Symposium on Computers and Communications

Y2 - 30 June 2022 through 3 July 2022

ER -

Anomaly Detection in Logical Sub-Views of WSNs

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Keywords

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