Clickbait Detection Using Deep Recurrent Neural Network
Razaque A. Alotaibi B. Alotaibi M. Hussain S. Alotaibi A. Jotsov V.
January-1 2022MDPI
Applied Sciences (Switzerland)
2022#12Issue 1
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.
Clickbait , Deep learning , Malicious links , Non-malicious links , RNN , Security
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Department of Computer Engineering and Cybersecurity, International Information Technology University, Almaty, 050000, Kazakhstan
Department of Information Technology, University of Tabuk, Tabuk, 47731, Saudi Arabia
Sensor Networks and Cellular Systems (SNCS) Research Center, University of Tabuk, Tabuk, 47731, Saudi Arabia
Department of Computer Science, Shaqra University, Shaqra, 11961, Saudi Arabia
Department of Computing, National University of Computer and Emerging Sciences, G-9/4, Islamabad, 44000, Pakistan
Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
Department of Information Technology, University of Library Studies and Information Technologies, Sofia, 17847, Bulgaria
Department of Computer Engineering and Cybersecurity
Department of Information Technology
Sensor Networks and Cellular Systems (SNCS) Research Center
Department of Computer Science
Department of Computing
Department of Computer Science
Department of Information Technology
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