A Deep-Learning-Driven Light-Weight Phishing Detection Sensor

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 19; p. 4258
• Main Authors: Wei, Bo, Hamad, Rebeen Ali, Yang, Longzhi, He, Xuan, Wang, Hao, Gao, Bin, Woo, Wai Lok
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 30-09-2019; MDPI
• Subjects: Algorithms; Blacklisting; cyber security; Cybersecurity; Deep learning; Feature selection; Machine learning; Malware; Methods; Neural networks; Phishing; phishing detection; Real time; Sensors; URLs; Websites; Weight reduction
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19194258

This paper designs an accurate and low-cost phishing detection sensor by exploring deep learning techniques. Phishing is a very common social engineering technique. The attackers try to deceive online users by mimicking a uniform resource locator (URL) and a webpage. Traditionally, phishing detection is largely based on manual reports from users. Machine learning techniques have recently been introduced for phishing detection. With the recent rapid development of deep learning techniques, many deep-learning-based recognition methods have also been explored to improve classification performance. This paper proposes a light-weight deep learning algorithm to detect the malicious URLs and enable a real-time and energy-saving phishing detection sensor. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. According to the experiments, the true detection rate has been improved. This paper has also verified that the proposed method can run in an energy-saving embedded single board computer in real-time.