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ThinkMind // CLOUD COMPUTING 2022, The Thirteenth International Conference on Cloud Computing, GRIDs, and Virtualization // View article cloud_computing_2022_1_20_28003
Design and Implementation of an Intelligent and Model-based Intrusion Detection System for IoT Networks
 Authors:
Peter Vogl
Sergei Weber
Julian Graf
Katrin Neubauer
Rudolf Hackenberg Keywords: Intrusion Detection, Artificial Intelligence, Machine Learning, Network Security, Internet of Things Abstract:
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 surveillance 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 network 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. Pages: 7 to 12 Copyright: Copyright (c) IARIA, 2022 Publication date: April 24, 2022 Published in: conference ISSN: 2308-4294 ISBN: 978-1-61208-948-5 Location: Barcelona, Spain Dates: from April 24, 2022 to April 28, 2022
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