Behavior Anomaly Detection in IoT Networks
Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). Cham: Springer International Publishing, 2020. p. 465-473. Lecture Notes on Data Engineering and Communications Technologies. vol. 49. ISSN 2367-4520. ISBN 978-3-030-43192-1.
Kapitola v knize
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.
Security Framework for IoT and Fog Computing Networks
Soukup, D.; Hujňák, O.; Štefunko, S.; Krejčí, R.; Grešák, E.
3rd International conference on I-SMAC. Piscataway, NJ: IEEE, 2019. p. 87-92. ISBN 978-1-7281-4365-1.
Stať ve sborníku vyzvaná či oceněná
Our environment becomes more and more in-tercon-nected. Various devices like refrigerators, doors or light bulbs communicate over different networks and provide information for applications that are supposed to make our lives easier and more comfortable. However, such data provide sensitive information about our presence or habits and become captivating for network attackers. It is very challenging to detect incidents in heterogeneous IoT networks where different devices come in and out or change their network profiles quite frequently. We propose a security framework for IoT and fog computing networks to address these challenges. Our framework is very flexible and designed even for devices with limited computational power. All components can be deployed on one network node or distributed among many, which also allows easy scalability. Part of our solution is software IoT gateway that provides the capability to analyse traffic from non-IP IoT sensors. This project covers full-stack security solution because it contains collectors, detectors and management tools. This framework has only software components with no relation to any specific hardware device. It is developed as an open-source project and it is publicly available for the worldwide community. Currently developed detectors detect identified vulnerabilities for Z-Wave, Long Range Wide Area Network (LoRaWAN), BLE and IP based IoT protocols.