Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers
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, monit...
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Online Access: | http://dx.doi.org/10.1155/2020/8694796 |
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doaj-2d42b804823c47eb97690b6fe99fc68d2020-11-25T03:57:22ZengHindawi-WileyComplexity1076-27871099-05262020-01-01202010.1155/2020/86947968694796Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM ClassifiersShan Wang0Sulaiman Khan1Chuyi Xu2Shah Nazir3Abdul Hafeez4School of Information Engineering, East China Jiao Tong University, Nanchang 330013, ChinaDepartment of Computer Science, University of Swabi, Swabi, PakistanSchool of Information Engineering, East China Jiao Tong University, Nanchang 330013, ChinaDepartment of Computer Science, University of Swabi, Swabi, PakistanDepartment of Computer Science, UET Jalozai, Jalozai, PakistanWith 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.http://dx.doi.org/10.1155/2020/8694796 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shan Wang Sulaiman Khan Chuyi Xu Shah Nazir Abdul Hafeez |
spellingShingle |
Shan Wang Sulaiman Khan Chuyi Xu Shah Nazir Abdul Hafeez Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers Complexity |
author_facet |
Shan Wang Sulaiman Khan Chuyi Xu Shah Nazir Abdul Hafeez |
author_sort |
Shan Wang |
title |
Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers |
title_short |
Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers |
title_full |
Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers |
title_fullStr |
Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers |
title_full_unstemmed |
Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers |
title_sort |
deep learning-based efficient model development for phishing detection using random forest and blstm classifiers |
publisher |
Hindawi-Wiley |
series |
Complexity |
issn |
1076-2787 1099-0526 |
publishDate |
2020-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2020/8694796 |
work_keys_str_mv |
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1715078967504928768 |