Bibliographic Details
| Title: |
Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification. |
| Authors: |
Brindha, R., Nandagopal, S., Azath, H., Sathana, V., Joshi, Gyanendra Prasad, Sung Won Kim |
| Source: |
Computers, Materials & Continua; 2023, Vol. 74 Issue 3, p5901-5914, 14p |
| Subject Terms: |
DEEP learning, PHISHING, INTERNET security, EMAIL, MACHINE learning, TELEPHONE calls, EMAIL security |
| Abstract: |
Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’ sensitive data. E-mails, instant messages and phone calls are some of the common modes used in cyberattacks. Though the security models are continuously upgraded to prevent cyberattacks, hackers find innovative ways to target the victims. In this background, there is a drastic increase observed in the number of phishing emails sent to potential targets. This scenario necessitates the importance of designing an effective classification model. Though numerous conventional models are available in the literature for proficient classification of phishing emails, the Machine Learning (ML) techniques and the Deep Learning (DL) models have been employed in the literature. The current study presents an Intelligent Cuckoo Search (CS) Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification (ICSOA-DLPEC) model. The aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing ones. At the initial stage, the pre-processing is performed through three stages such as email cleaning, tokenization and stop-word elimination. Then, the N-gram approach is;moreover, the CS algorithm is applied to extract the useful feature vectors. Moreover, the CS algorithm is employed with the Gated Recurrent Unit (GRU)model to detect and classify phishing emails. Furthermore, the CS algorithm is used to fine-tune the parameters involved in the GRU model. The performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset, and the results were assessed under several dimensions. Extensive comparative studies were conducted, and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing approaches. The proposed model achieved a maximum accuracy of 99.72%. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
