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The ongoing digitization and digitalization entails the increasing risk of privacy breaches through cyber attacks. Internet of Things (IoT) environments often contain devices monitoring sensitive data such as vital signs, movement or surveil-lance data. Unfortunately, many of these devices provide limited security features. The purpose of this paper is to investigate how artificial intelligence and static analysis can be implemented in practice-oriented intelligent Intrusion Detection Systems to monitor IoT networks. In addition, the question of how static and dynamic methods can be developed and combined to improve net-work attack detection is discussed. The implementation concept is based on a layer-based architecture with a modular deployment of classical security analysis and modern artificial intelligent methods. To extract important features from the IoT network data a time-based approach has been developed. Combined with
network metadata these features enhance the performance of the artificial intelligence driven anomaly detection and attack classification. The paper demonstrates that artificial intelligence
and static analysis methods can be combined in an intelligent Intrusion Detection System to improve the security of IoT environments.
Forschung 2019
(2019)