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Enhancing detection of malicious profiles and spam tweets with an automated honeypot framework powered by deep learning

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Abstract

Social networks are widely used platforms for sharing various information and content, including text, images, and videos.The main challenge in social networking today is the verification of data credibility and the identification of genuine social media profiles. Cybercriminals take advantage of this by utilizing fake profiles to disseminate false information. However, current research mainly concentrates on spam filtering and analyzing malicious behavior separately, disregarding the interrelated nature of these issues. We propose a more desirable global, hybrid solution that encompasses both malicious profile detection and spam detection to mitigate the spread of spam effectively. This paper offers a deep learning-based method for detecting malicious profiles and spam tweets. For the profiles to interact with them as legitimate profiles, we first provide the detection of fake profiles using an automated honeypot. Next, we detect those who make interactions as malicious profiles, and finally, we collect their shared content to find spam tweets using a convolution neural network algorithm. We suggest using collaborative filtering and content filtering algorithms from recommender systems to define accounts similar to harmful profiles and spam similar to spam material picked up by convolution neural networks. We get a highly compelling and intriguing outcome with higher accuracy (99.23%) and lesser loss than typical learning algorithms.

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Authors and Affiliations

  1. Image Laboratory, School of Technology, Moulay Ismail University of Meknes, Meknes, Morocco

    Fatna El Mendili & Mohammed Fattah

  2. Engineering Sciences Laboratory, National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco

    Nisrine Berros, Youness Filaly & Younès El Bouzekri El Idrissi

Authors
  1. Fatna El Mendili
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  2. Mohammed Fattah
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  3. Nisrine Berros
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  4. Youness Filaly
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  5. Younès El Bouzekri El Idrissi
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Contributions

we declare that the manuscript written by the following authors: Fatna ELmendili (F.E) Mohammed Fattah (M.F) Nisrine Beross (N.B) Younes Fillaly (Y.F) Younès EL BOUZEKRI EL IDRISSI (Y.E.E) F.E, M.F and N.B wrote the main text of the manuscript and Y.F and Y.E.E prepared all figures and tables. All authors reviewed the manuscript.

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Correspondence to Fatna El Mendili.

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El Mendili, F., Fattah, M., Berros, N. et al. Enhancing detection of malicious profiles and spam tweets with an automated honeypot framework powered by deep learning. Int. J. Inf. Secur. 23, 1359–1388 (2024). https://doi.org/10.1007/s10207-023-00796-7

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