Character-level word encoding deep learning model for combating cyber threats in phishing URL detection. (May 2022)

• Record Type: Journal Article
• Authors: Alshehri, Mohammed; Abugabah, Ahed; Algarni, Abdullah; Almotairi, Sultan
• Is Part Of: Computers & electrical engineering. Volume 100(2022)
• Publication Date: 2022-05
• Subjects: cyber threat -- deep learning -- machine learning -- phishing -- deep learning model; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107868
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Cyber-crimes reported in the last quarter globally, 21% fall into the phishing category character-level encoding decreases the discrete workspace and can be effective even in an energy-constrained environment lightweight deep learning model for the detection of malicious URLs use of character-level embedding was performed, and it was found that the execution time was 30% ABSTRACT: A cyber threat is generally a malicious activity that damages or steals data, or something that disrupts digital life. Such threats include viruses, security breaches, DoS attacks, and data theft. Phishing is a type of cyber threat whereby the attackers mimic a genuine URL or a webpage and steal user data, 21% fall into the phishing category. The novel approach of using the character-level encoding of URLs is introduced. Unlike word-level encoding, the use of character-level encoding decreases the discrete workspace and can be effective even in an energy-constrained environment. The experimental results of comparisons to other state-of-the-art methods demonstrate that the proposed method achieved 98.12% of true positive instances. Moreover, Conclusions: An experimental evaluation was performed to demonstrate the efficiency, and it was observed that the accuracy reached an all-time high of 98.13%. the experiments prove that the proposed method can operate efficiently even in energy-saving modes of phishing detection systems. Graphical abstract: Image, graphical abstract