Abstract
Wireless sensor and actuator networks are essential components of modern technologies and infrastructures for smart homes and cities, intelligent transportation systems, advanced manufacturing, Internet of things and, for example, fog and edge computing. Cybersecurity of such massively distributed systems is becoming a major issue, and advanced methods to improve their safety and reliability are needed. Intrusion detection systems automatically identify malicious network traffic, uncover cybernetic attacks and notify network users and operators. In this work, a novel strategy for intrusion detection in wireless sensor networks based on accurate neural models of specific attacks learned from network traffic data is proposed and evaluated.
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Acknowledgements
This work was supported by the European Regional DevelopmentFund in the Research Centre of Advanced Mechatronic Systems project, project number CZ.02.1.01/0.0/0.0/16_019/0000867 within the Operational Programme Research, Development and Education, and by the projects SP2019/135 and SP2019/141 of the Student Grant System, VSB—Technical University of Ostrava.
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Batiha, T., Prauzek, M., Krömer, P. (2020). Intrusion Detection in Wireless Sensor Networks by an Ensemble of Artificial Neural Networks. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol 142. Springer, Singapore. https://doi.org/10.1007/978-981-13-8311-3_28
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DOI: https://doi.org/10.1007/978-981-13-8311-3_28
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