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Behavior Anomaly Detection in IoT Networks

Conference paper
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Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 49)

Abstract

Data encryption makes deep packet inspection less suitable nowadays, and the need of analyzing encrypted traffic is growing. Machine learning brings new options to recognize a type of communication despite the heterogeneity of encrypted IoT traffic right at the network edge. We propose the design of scalable architecture and the method for behavior anomaly detection in IoT networks. Combination of two existing semi-supervised techniques that we used ensures higher reliability of anomaly detection and improves results achieved by a single method. We describe conducted classification and anomaly detection experiments allowed thanks to existing and our training datasets. Presented satisfying results provide a subject for further work and allow us to elaborate on this idea.

Keywords

IoT behavioral analysis Encrypted traffic Anomaly detection 

Notes

Acknowledgment

This work was supported by the Grant Agency of the Czech Technical University in Prague, grant No. SGS17/212/OHK3/3T/18 funded by the Ministry of Education, Youth and Sports of the Czech Republic and Secure Gateway for Internet of Things (SIoT) project No. VI20172020079 funded by the Ministry of the Interior of the Czech Republic.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.CESNET, a.l.e.PragueCzech Republic
  2. 2.CTU in PraguePragueCzech Republic

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