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Detection of Phishing in Internet-of-Things Using Hybrid Deep Belief Network

S. Ashwini*, S. Magesh Kumar

Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (Deemed to be University), Chennai, Tamilnadu, India

* Corresponding Author: S. Ashwini. Email: email

Intelligent Automation & Soft Computing 2023, 36(3), 3043-3056.


Increase in the use of internet of things owned devices is one of the reasons for increased network traffic. While connecting the smart devices with publicly available network many kinds of phishing attacks are able to enter into the mobile devices and corrupt the existing system. The Phishing is the slow and resilient attack stacking techniques probe the users. The proposed model is focused on detecting phishing attacks in internet of things enabled devices through a robust algorithm called Novel Watch and Trap Algorithm (NWAT). Though Predictive mapping, Predictive Validation and Predictive analysis mechanism is developed. For the test purpose Canadian Institute of cyber security (CIC) dataset is used for creating a robust prediction model. This attack generates a resilience corruption works that slowly gathers the credential information from the mobiles. The proposed Predictive analysis model (PAM) enabled NWAT algorithm is used to predict the phishing probes in the form of suspicious process happening in the IoT networks. The prediction system considers the peer-to-peer communication window open for the established communication, the suspicious process and its pattern is identified by the new approach. The proposed model is validated by finding the prediction accuracy, Precision, recalls F1score, error rate, Mathew’s Correlation Coefficient (MCC) and Balanced Detection Rate (BDR). The presented approach is comparatively analyzed with the state-of-the-art approach of existing system related to various types of Phishing probes.


Cite This Article

S. Ashwini and S. M. Kumar, "Detection of phishing in internet-of-things using hybrid deep belief network," Intelligent Automation & Soft Computing, vol. 36, no.3, pp. 3043–3056, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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