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ARTICLE

Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

Ashit Kumar Dutta1,*, T. Meyyappan2, Basit Qureshi3, Majed Alsanea4, Anas Waleed Abulfaraj5, Manal M. Al Faraj1, Abdul Rahaman Wahab Sait6
1 Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, 13713, Kingdom of Saudi Arabia
2 Department of Computer Science, Alagappa University, Karaikudi, 630003, India
3 Department of Computer Science, Prince Sultan University, Riyadh, 11586, Kingdom of Saudi Arabia
4 Department of Computing, Arabeast Colleges, Riyadh, 11583, Kingdom of Saudi Arabia
5 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 23613, Kingdom of Saudi Arabia
6 Department of Archives and Communication, King Faisal University, Al Ahsa, Hofuf, 31982, Kingdom of Saudi Arabia
* Corresponding Author: Ashit Kumar Dutta. Email:

Computer Systems Science and Engineering 2023, 44(3), 2701-2713. https://doi.org/10.32604/csse.2023.028984

Received 22 February 2022; Accepted 30 March 2022; Issue published 01 August 2022

Abstract

Recently, developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day lives. It results in illegal access to users’ private data and compromises it. Phishing is a popular attack which tricked the user into accessing malicious data and gaining the data. Proper identification of phishing emails can be treated as an essential process in the domain of cybersecurity. This article focuses on the design of biogeography based optimization with deep learning for Phishing Email detection and classification (BBODL-PEDC) model. The major intention of the BBODL-PEDC model is to distinguish emails between legitimate and phishing. The BBODL-PEDC model initially performs data pre-processing in three levels namely email cleaning, tokenization, and stop word elimination. Besides, TF-IDF model is applied for the extraction of useful feature vectors. Moreover, optimal deep belief network (DBN) model is used for the email classification and its efficacy can be boosted by the BBO based hyperparameter tuning process. The performance validation of the BBODL-PEDC model can be performed using benchmark dataset and the results are assessed under several dimensions. Extensive comparative studies reported the superior outcomes of the BBODL-PEDC model over the recent approaches.

Keywords

Cybersecurity; phishing email; data classification; deep learning; biogeography based optimization; hyperparameter tuning

Cite This Article

A. K. Dutta, T. Meyyappan, B. Qureshi, M. Alsanea, A. W. Abulfaraj et al., "Optimal deep belief network enabled cybersecurity phishing email classification," Computer Systems Science and Engineering, vol. 44, no.3, pp. 2701–2713, 2023.



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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