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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A Deep-Learning-Driven Light-Weight Phishing Detection Sensor. / Wei, Bo; Hamad, Rebeen Ali; Yang, Longzhi et al.
In: Sensors, Vol. 19, No. 19, 4258, 30.09.2019.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Wei, B, Hamad, RA, Yang, L, He, X, Wang, H, Gao, B & Woo, WL 2019, 'A Deep-Learning-Driven Light-Weight Phishing Detection Sensor', Sensors, vol. 19, no. 19, 4258. https://doi.org/10.3390/s19194258

APA

Wei, B., Hamad, R. A., Yang, L., He, X., Wang, H., Gao, B., & Woo, W. L. (2019). A Deep-Learning-Driven Light-Weight Phishing Detection Sensor. Sensors, 19(19), Article 4258. https://doi.org/10.3390/s19194258

Vancouver

Wei B, Hamad RA, Yang L, He X, Wang H, Gao B et al. A Deep-Learning-Driven Light-Weight Phishing Detection Sensor. Sensors. 2019 Sept 30;19(19):4258. doi: 10.3390/s19194258

Author

Wei, Bo ; Hamad, Rebeen Ali ; Yang, Longzhi et al. / A Deep-Learning-Driven Light-Weight Phishing Detection Sensor. In: Sensors. 2019 ; Vol. 19, No. 19.

Bibtex

@article{1622415ea48340fabdd89547f93208c2,
title = "A Deep-Learning-Driven Light-Weight Phishing Detection Sensor",
abstract = "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.",
keywords = "phishing detection, cyber security, deep learning",
author = "Bo Wei and Hamad, {Rebeen Ali} and Longzhi Yang and Xuan He and Hao Wang and Bin Gao and Woo, {Wai Lok}",
year = "2019",
month = sep,
day = "30",
doi = "10.3390/s19194258",
language = "English",
volume = "19",
journal = "Sensors",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "19",

}

RIS

TY - JOUR

T1 - A Deep-Learning-Driven Light-Weight Phishing Detection Sensor

AU - Wei, Bo

AU - Hamad, Rebeen Ali

AU - Yang, Longzhi

AU - He, Xuan

AU - Wang, Hao

AU - Gao, Bin

AU - Woo, Wai Lok

PY - 2019/9/30

Y1 - 2019/9/30

N2 - 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.

AB - 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.

KW - phishing detection

KW - cyber security

KW - deep learning

U2 - 10.3390/s19194258

DO - 10.3390/s19194258

M3 - Journal article

VL - 19

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 19

M1 - 4258

ER -