Anomaly detection is a machine learning task that has been investigated within diverse research areas and application domains. In this paper, we performed anomaly detection for Physical Threat Intelligence. Specifically, we performed anomaly detection for air pollution and public transport traffic analysis for the city of Oslo, Norway. To this aim, the state-of-the-art method SparkGHSOM was considered to learn predictive models for normal (i.e. regular) scenarios of air quality and traffic jams in a distributed fashion. Furthermore, we extended the main algorithm to make the detected anomalies explainable through an instance-based feature ranking approach. The results showed that SparkGHSOM is able to detect anomalies for both the real applications considered in this study, despite the fact it was designed for different tasks.

Anomaly Detection for Physical Threat Intelligence

Paolo Mignone
;
Donato Malerba;Michelangelo Ceci
2023-01-01

Abstract

Anomaly detection is a machine learning task that has been investigated within diverse research areas and application domains. In this paper, we performed anomaly detection for Physical Threat Intelligence. Specifically, we performed anomaly detection for air pollution and public transport traffic analysis for the city of Oslo, Norway. To this aim, the state-of-the-art method SparkGHSOM was considered to learn predictive models for normal (i.e. regular) scenarios of air quality and traffic jams in a distributed fashion. Furthermore, we extended the main algorithm to make the detected anomalies explainable through an instance-based feature ranking approach. The results showed that SparkGHSOM is able to detect anomalies for both the real applications considered in this study, despite the fact it was designed for different tasks.
2023
978-3-031-23617-4
978-3-031-23618-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/418178
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