Multimodel Phishing URL Detection Using LSTM, Bidirectional LSTM, and GRU Models

• Published in: Future internet Vol. 14; no. 11; p. 340
• Main Authors: Roy, Sanjiban Sekhar, Awad, Ali Ismail, Amare, Lamesgen Adugnaw, Erkihun, Mabrie Tesfaye, Anas, Mohd
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2022
• Subjects: Accuracy; Algorithms; Artificial intelligence; bidirectional LSTM (Bi-LSTM); Classification; Cybercrime; Data mining; Datasets; Deep learning; Electronic mail systems; Feature selection; gated recurrent unit (GRU) RNN; Internet; long short-term memory (LSTM); Machine learning; Neural networks; Personal information; Phishing; phishing URL detection; Recurrent neural networks; URLs; Websites
• ISSN: 1999-5903; 1999-5903
• DOI: 10.3390/fi14110340

In today’s world, phishing attacks are gradually increasing, resulting in individuals losing valuables, assets, personal information, etc., to unauthorized parties. In phishing, attackers craft malicious websites disguised as well-known, legitimate sites and send them to individuals to steal personal information and other related private details. Therefore, an efficient and accurate method is required to determine whether a website is malicious. Numerous methods have been proposed for detecting malicious uniform resource locators (URLs) using deep learning, machine learning, and other approaches. In this study, we have used malicious and benign URLs datasets and have proposed a detection mechanism for detecting malicious URLs using recurrent neural network models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and the gated recurrent unit (GRU). Experimental results have shown that the proposed mechanism achieved an accuracy of 97.0% for LSTM, 99.0% for Bi-LSTM, and 97.5% for GRU, respectively.