Clickbait Detection Using Deep Recurrent Neural Network
People who use social networks often fall prey to clickbait, which is commonly exploited by scammers. The scammer attempts to create a striking headline that attracts the majority of users to click an attached link. Users who follow the link can be redirected to a fraudulent resource, where their pe...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/1/504 |
| _version_ | 1827668737825701888 |
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| author | Abdul Razaque Bandar Alotaibi Munif Alotaibi Shujaat Hussain Aziz Alotaibi Vladimir Jotsov |
| author_facet | Abdul Razaque Bandar Alotaibi Munif Alotaibi Shujaat Hussain Aziz Alotaibi Vladimir Jotsov |
| author_sort | Abdul Razaque |
| collection | DOAJ |
| description | People who use social networks often fall prey to clickbait, which is commonly exploited by scammers. The scammer attempts to create a striking headline that attracts the majority of users to click an attached link. Users who follow the link can be redirected to a fraudulent resource, where their personal data are easily extracted. To solve this problem, a novel browser extension named ClickBaitSecurity is proposed, which helps to evaluate the security of a link. The novel extension is based on the legitimate and illegitimate list search (LILS) algorithm and the domain rating check (DRC) algorithm. Both of these algorithms incorporate binary search features to detect malicious content more quickly and more efficiently. Furthermore, ClickBaitSecurity leverages the features of a deep recurrent neural network (RNN). The proposed ClickBaitSecurity solution has greater accuracy in detecting malicious and safe links compared to existing solutions. |
| first_indexed | 2024-03-10T03:48:51Z |
| format | Article |
| id | doaj.art-f9a0254e99bb4d65b11d465c66ab4ffb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:48:51Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f9a0254e99bb4d65b11d465c66ab4ffb2023-11-23T11:14:04ZengMDPI AGApplied Sciences2076-34172022-01-0112150410.3390/app12010504Clickbait Detection Using Deep Recurrent Neural NetworkAbdul Razaque0Bandar Alotaibi1Munif Alotaibi2Shujaat Hussain3Aziz Alotaibi4Vladimir Jotsov5Department of Computer Engineering and Cybersecurity, International Information Technology University, Almaty 050000, KazakhstanDepartment of Information Technology, University of Tabuk, Tabuk 47731, Saudi ArabiaDepartment of Computer Science, Shaqra University, Shaqra 11961, Saudi ArabiaDepartment of Computing, National University of Computer and Emerging Sciences, G-9/4, Islamabad 44000, PakistanDepartment of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Computer Engineering and Cybersecurity, International Information Technology University, Almaty 050000, KazakhstanPeople who use social networks often fall prey to clickbait, which is commonly exploited by scammers. The scammer attempts to create a striking headline that attracts the majority of users to click an attached link. Users who follow the link can be redirected to a fraudulent resource, where their personal data are easily extracted. To solve this problem, a novel browser extension named ClickBaitSecurity is proposed, which helps to evaluate the security of a link. The novel extension is based on the legitimate and illegitimate list search (LILS) algorithm and the domain rating check (DRC) algorithm. Both of these algorithms incorporate binary search features to detect malicious content more quickly and more efficiently. Furthermore, ClickBaitSecurity leverages the features of a deep recurrent neural network (RNN). The proposed ClickBaitSecurity solution has greater accuracy in detecting malicious and safe links compared to existing solutions.https://www.mdpi.com/2076-3417/12/1/504clickbaitsecuritymalicious linksnon-malicious linksdeep learningRNN |
| spellingShingle | Abdul Razaque Bandar Alotaibi Munif Alotaibi Shujaat Hussain Aziz Alotaibi Vladimir Jotsov Clickbait Detection Using Deep Recurrent Neural Network Applied Sciences clickbait security malicious links non-malicious links deep learning RNN |
| title | Clickbait Detection Using Deep Recurrent Neural Network |
| title_full | Clickbait Detection Using Deep Recurrent Neural Network |
| title_fullStr | Clickbait Detection Using Deep Recurrent Neural Network |
| title_full_unstemmed | Clickbait Detection Using Deep Recurrent Neural Network |
| title_short | Clickbait Detection Using Deep Recurrent Neural Network |
| title_sort | clickbait detection using deep recurrent neural network |
| topic | clickbait security malicious links non-malicious links deep learning RNN |
| url | https://www.mdpi.com/2076-3417/12/1/504 |
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