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Design and Implementation of an Intelligent and Model-based Intrusion Detection System for Iot Networks
- 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.
Author: | Peter Vogl, Sergei Weber, Julian Graf, Katrin Neubauer, Rudolf Hackenberg |
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URL / DOI: | https://www.thinkmind.org/index.php?view=article&articleid=cloud_computing_2022_1_20_28003 |
ISBN: | 978-1-61208-948-5 |
Parent Title (English): | Cloud Computing 2022: The Thirteenth International Conference on Cloud Computing, GRIDs, and Virtualization, Special Track FAST-CSP, Barcelona, Spain, 24.-28.04.2022 |
Publisher: | IARIA |
Place of publication: | [Wilmington, DE, USA] |
Document Type: | conference proceeding (article) |
Language: | English |
Year of first Publication: | 2022 |
Release Date: | 2022/06/22 |
Tag: | Artificial Intelligence; Internet of Things; Intrusion Detection; Machine Learning; Network Security |
First Page: | 7 |
Last Page: | 12 |
Institutes: | Fakultät Informatik und Mathematik |
Fakultät Informatik und Mathematik / Labor Informationssicherheit und Complience (ISC) | |
Begutachtungsstatus: | peer-reviewed |
research focus: | Digitalisierung |
Licence (German): | Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG |