Abstract
Security is a significant issue with ubiquitous connectivity, more so with the widespread adoption of Internet of Things (IoT). The novelty of attacks with each passing day poses a conundrum to the organizations and sectors deploying the IoT. The fact remains that conventional cybersecurity frameworks face the trouble of distinguishing unknown attacks in most scenarios. Recent studies explore the endless capabilities of machine learning (ML) in reinstating the security of the IoT infrastructure. ML includes the capability of self-study and training for discovering the path for security breach and attack detection. The training methodology and progression in ML is superior to centralized detection systems. A novel methodology of distributed training on fog node in the IoT architecture with exchange of parameters (DT-FN) enhancement of intelligence through machine learning is proposed in this paper. The analyses have demonstrated that appropriated assault recognition framework has outsmarted the incorporated discovery frameworks utilizing ML model. Intrusion detection system of IoT has been incorporated on DL for more effectiveness and identification of higher level security threats. Target prejudgement-based interruption identification framework for IoT has also been discussed. The key metrics that are considered are detection rate, detection accuracy, false alarm rate, F1 measurement, precision and recall.
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27 December 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s00500-022-07781-7
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Sugi, S.S.S., Ratna, S.R. RETRACTED ARTICLE: A novel distributed training on fog node in IoT backbone networks for security. Soft Comput 24, 18399–18410 (2020). https://doi.org/10.1007/s00500-020-05047-8
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DOI: https://doi.org/10.1007/s00500-020-05047-8