Abstract
With the increase in the number of electronic devices and developments in the communication system, security becomes one of the challenging issues. Users are interacting with each other through different heterogeneous devices such as smart sensors, actuators, and many other devices to process, monitor, and communicate different scenarios of real life. Such communication needs a secure medium through which users can communicate in a secure and reliable way so that their information may not be lost. The proposed study is an endeavor toward the detection of phishing by using random forest and BLSTM classifiers. The experimental results of the proposed study are promising in phishing detection, and the study reflects the applicability of the proposed algorithms in the information security. The experimental results show that the BLSTM-based phishing detection model is prominent in ensuring the network security by generating a recognition rate of 95.47% compared to the conventional RF-based model that generates a recognition rate of 87.53%. This high recognition rate for the BLSTM-based model reflects the applicability of the proposed model for phishing detection.
Highlights
Research ArticleReceived 25 July 2020; Revised 11 September 2020; Accepted 14 September 2020; Published 24 September 2020
Study is section of the paper presents the related work reported in the proposed field and the background information about the deep learning-based bidirectional long short-term memory (BLSTM) model and the random forest. e following sections briefly show the details of the background study.2.1
Users are interacting with each other through different heterogeneous devices such as smart sensors, actuators, and many other devices to process, monitor, and communicate different scenarios of real life. Such communication needs a secure medium through which users can communicate in a secure and reliable way so that their information may not be lost. e proposed study is an endeavor toward the detection of phishing by using random forest and BLSTM classifiers. e experimental results of the proposed study are promising in phishing detection, and the study reflects the applicability of the proposed algorithms in the information security. e experimental results show that the BLSTM-based phishing detection model is prominent in ensuring the network security by generating a recognition rate of 95.47% compared to the conventional RF-based model that generates a recognition rate of 87.53%. is high recognition rate for the BLSTM-based model reflects the applicability of the proposed model for phishing detection
Summary
Received 25 July 2020; Revised 11 September 2020; Accepted 14 September 2020; Published 24 September 2020. E industries such as Internet of Medical ings (IoMT) allow to reduce the unnecessary visits to the hospital and alleviate burden on the medical care system by providing connectivity over a secure network between medical experts and patients. By doing so, they can save a lot of time and money [14, 15]. After validating the applicability of the proposed model for different phishing datasets, it was concluded that the BLSTM-based phishing detection model is prominent in ensuring the network security by generating a recognition rate of 95.47% compared to the conventional RF-based model that generates a recognition rate of 87.53%.
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