A Deep-Learning-Driven Light-Weight Phishing Detection Sensor
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 detectio...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/19/4258 |
| _version_ | 1811301263295381504 |
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| author | Bo Wei Rebeen Ali Hamad Longzhi Yang Xuan He Hao Wang Bin Gao Wai Lok Woo |
| author_facet | Bo Wei Rebeen Ali Hamad Longzhi Yang Xuan He Hao Wang Bin Gao Wai Lok Woo |
| author_sort | Bo Wei |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T07:05:39Z |
| format | Article |
| id | doaj.art-95a3cea52fa84b1e87c953b2d7105f41 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:05:39Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-95a3cea52fa84b1e87c953b2d7105f412022-12-22T02:57:00ZengMDPI AGSensors1424-82202019-09-011919425810.3390/s19194258s19194258A Deep-Learning-Driven Light-Weight Phishing Detection SensorBo Wei0Rebeen Ali Hamad1Longzhi Yang2Xuan He3Hao Wang4Bin Gao5Wai Lok Woo6Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UKDepartment of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UKDepartment of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UKSchool of Sino-Dutch Biomedical & Information Engineering, Northeastern University, Shenyang 110169, ChinaAutomation College, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaDepartment of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UKThis 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.https://www.mdpi.com/1424-8220/19/19/4258phishing detectioncyber securitydeep learning |
| spellingShingle | Bo Wei Rebeen Ali Hamad Longzhi Yang Xuan He Hao Wang Bin Gao Wai Lok Woo A Deep-Learning-Driven Light-Weight Phishing Detection Sensor Sensors phishing detection cyber security deep learning |
| title | A Deep-Learning-Driven Light-Weight Phishing Detection Sensor |
| title_full | A Deep-Learning-Driven Light-Weight Phishing Detection Sensor |
| title_fullStr | A Deep-Learning-Driven Light-Weight Phishing Detection Sensor |
| title_full_unstemmed | A Deep-Learning-Driven Light-Weight Phishing Detection Sensor |
| title_short | A Deep-Learning-Driven Light-Weight Phishing Detection Sensor |
| title_sort | deep learning driven light weight phishing detection sensor |
| topic | phishing detection cyber security deep learning |
| url | https://www.mdpi.com/1424-8220/19/19/4258 |
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