An Architecture for Enabling Collective Intelligence in IoT Networks
Frantti, T., & Şafak, I. (2023). An Architecture for Enabling Collective Intelligence in IoT Networks. In N. T. Nguyen, J. Botzheim, L. Gulyás, M. Núñez, J. Treur, G. Vossen, & A. Kozierkiewicz (Eds.), Computational Collective Intelligence : 15th International Conference, ICCCI 2023, Budapest, Hungary, September 27–29, 2023, Proceedings (pp. 29-42). Springer. Lecture Notes in Computer Science, 14162. https://doi.org/10.1007/978-3-031-41456-5_3
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2023Copyright
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
Proliferation of the Internet of Things (IoT) has fundamentally changed how different application environments are being used. IoT networks are prone to malicious attacks similar to other networks. Additionally, physical tampering, injection and capturing of the nodes are more probable in IoT networks. Therefore, conventional security practices require substantial re-engineering for IoT networks. Here we present an architecture that enables collective intelligence for IoT networks via smart network nodes and blockchain technology. In this architecture, various security related functionalities are distributed to network nodes to detect tampered, captured and injected devices, recognize their movements and prevent networks’ use as an attack surface. Nodes interact with signaling, security information and data traffic. Security information aids to distribute cyber-security functionalities across the IoT network based on the device and/or application type. Every node in the proposed IoT network does not need to have all the cyber-security functionalities, but the network as a whole needs these functionalities.
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International Conference on Computational Collective IntelligenceIs part of publication
Computational Collective Intelligence : 15th International Conference, ICCCI 2023, Budapest, Hungary, September 27–29, 2023, ProceedingsISSN Search the Publication Forum
0302-9743Keywords
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https://converis.jyu.fi/converis/portal/detail/Publication/184940173
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