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SecureFlow: Knowledge and data-driven ensemble for intrusion detection and dynamic rule configuration in software-defined IoT environment

Singh, Amritpal; Chouhan, Pushpinder Kaur; Aujla, Gagangeet Singh

SecureFlow: Knowledge and data-driven ensemble for intrusion detection and dynamic rule configuration in software-defined IoT environment Thumbnail


Authors

Pushpinder Kaur Chouhan



Abstract

There is a massive growth in the rate of heterogeneous devices configured in the Internet of Things (IoT) environment for efficient communication. The IoT devices are limited in resources, and there are no defined protocols in terms of security during communication in the IoT-based platforms. Several solutions are framed to make communication secure in the IoT ecosystem. However, the existing schemes need to be more reliable to handle the cyber threats and unwarranted incidents (such as intrusions, anomalies and attacks) coming from IoT endpoints owing to the unstructured patterns of IoT data and dynamic network conditions. Moreover, heavy cryptographic primitives have their deployment challenges due to the resource constraints of the IoT ecosystem. The dynamic nature of IoT traffic requires flexible and varied rules to handle the threats in different deployment scenarios. Therefore, a programmable interface enabled through Software-defined Networking (SDN) can handle heterogeneous threats and incidents in the IoT cyber world. Thus, in this paper, we have designed a novel framework, SecureFlow, an intrusion detection and dynamic rule configuration system based on the knowledge-based and data-driven ensemble. The proposed framework is robust and fault tolerant owing to dual-layer Intrusion Detection System (IDS) and rule configuration modules that can work without one of them. SecureFlow validated through several experiments performed through emulations in Mininet. The results depict that the proposed framework is effective and promising.

Citation

Singh, A., Chouhan, P. K., & Aujla, G. S. (2024). SecureFlow: Knowledge and data-driven ensemble for intrusion detection and dynamic rule configuration in software-defined IoT environment. Ad Hoc Networks, 156, Article 103404. https://doi.org/10.1016/j.adhoc.2024.103404

Journal Article Type Article
Acceptance Date Jan 10, 2010
Online Publication Date Jan 20, 2024
Publication Date Apr 1, 2024
Deposit Date Mar 6, 2024
Publicly Available Date Mar 6, 2024
Journal Ad Hoc Networks
Print ISSN 1570-8705
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 156
Article Number 103404
DOI https://doi.org/10.1016/j.adhoc.2024.103404
Keywords Computer Networks and Communications; Hardware and Architecture; Software
Public URL https://durham-repository.worktribe.com/output/2311362

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