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Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers
Joint Authors
Khan, Sulaiman
Nazir, Shah
Wang, Shan
Xu, Chuyi
Hafeez, Abdul
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-24
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Shan& Khan, Sulaiman& Xu, Chuyi& Nazir, Shah& Hafeez, Abdul. 2020. Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers. Complexity،Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1144507
Modern Language Association (MLA)
Wang, Shan…[et al.]. Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers. Complexity No. 2020 (2020), pp.1-7.
https://search.emarefa.net/detail/BIM-1144507
American Medical Association (AMA)
Wang, Shan& Khan, Sulaiman& Xu, Chuyi& Nazir, Shah& Hafeez, Abdul. Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers. Complexity. 2020. Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1144507
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1144507