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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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References

  1. Wu, T., Wen, S., Liu, S., Zhang, J., Xiang, Y., Alrubaian, M., Hassan, M.M.: Detecting Spamming Activities in Twitter Based on Deep-Learning Technique. Wiley, New York (2017)

    Book  Google Scholar 

  2. Eshete, B., Villafiorita, A., Weldemariam, K.,Binspect: holistic analysis and detection of malicious web pages. In: Security and Privacy in Communication Networks, pp. 149–166. Springer (2013)

  3. Aggarwal, A., Rajadesingan, A., Kumaraguru, P.: Phishari: automatic real-time phishing detection on Twitter. In: eCrime Researchers Summit (eCrime), 2012, pp. 1–12. IEEE (2012)

  4. Eshete, B., Villafiorita, A., Weldemariam, K.: Einspect: Evolution-guided analaysis and detection of malicious web pages. Fondazione Bruno Kessler, Technical Report (2012)

  5. Rahman, M.S., Huang, T.-K., Madhyastha, H.V., Faloutsos, M.: Efficient and scalable socware detection in online social networks. In: USENIX Security (2012)

  6. Kemp, S.: Digital in 2017: Global Overview, accessed on Jan. 24 (2017). https://wearesocial.com/special-reports/digital-in-2017-global-overview

  7. ISACA. Advanced Persistent Threat Awareness, accessed on (2013). http://www.trendmicro.com/cloudcontent/us/pdfs/business/datasheets/wp_apt-survey-report.pdf

  8. Ahmad. How Many Internet and #SocialMedia Users are Fake? accessed on Apr. 2 (2015). http://www.digitalinformationworld.com/2015/04/infographic-how-many-internetsusers-are-fake.html

  9. Neeraja, M., Prakash, J.: Computer science and engineering, MITE Moodabidri, India, detecting malicious posts in social networks using text analysis. Int. J. Sci. Res. (IJSR) 5(6) (2016)

  10. Jasek, R., Kolarik, M., Vymola, T.: APT detection system using honeypots. In: Proceedings of the 13th International Conference on Application Information Communication (AIC), pp. 25–29 (2013)

  11. Paradise, A., et al.: Cration et gestion de pots de miel de rseaux sociaux pour dtecter les cyberattaques cibles. dans IEEE Trans. Comput. Soc. Syst. 4(3), 65–79 (2017) https://doi.org/10.1109/TCSS.2017.2719705

  12. Zhu, Q., Clark, A., Poovendran, R., Baar, T.: Deployment and exploitation of deceptive honeybots in social networks. In: 52nd IEEE Conference on Decision and Control, Firenze, Italy, pp. 212–219 (2013) https://doi.org/10.1109/CDC.2013.6759884

  13. Webb, S., Caverlee, J., Pu, C.: Social honeypots: Making friends with a spammer near you, presented at the CEAS. California, CA, USA (2008)

  14. Lee, K., Eoff, B.D., Caverlee, J.: Seven months with the devils: a long-term study of content polluters on twitter. In: Proceedings, pp. 1–8. Barcelona, Spain, Jul, ICWSM (2011)

  15. Egele, M., Stringhini, G., Kruegel, C., Vigna, G.: Compa: Detecting compromised accounts on social networks. In: NDSS (2013)

  16. Martinez-Romo, J., Araujo, L.: Detecting malicious tweets in trending topics using a statistical analysis of language. Expert Syst. Appl. 40(8), 29923000 (2013)

    Article  Google Scholar 

  17. Yardi, S., Romero, D., Schoenebeck, G., et al.: Detecting spam in a twitter network. First Monday 15(1)

  18. Egele, M., Stringhini, G., Kruegel, C., Vigna, G.: Compa: Detecting compromised accounts on social networks. In: NDSS (2013)

  19. Gao, H., Yang, Y., Bu, K., Chen, Y., Downey, D., Lee, K., Choudhary, A.: Spam ain’t as diverse as it seems: throttling osn spam with templates underneath. In: Proceedings of the 30th Annual Computer Security Applications Conference, pp. 7685. ACM (2014)

  20. Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. SIGKDD Explor. Newsl. 19(1), 22–36 (2017). https://doi.org/10.1145/3137597.3137600

    Article  Google Scholar 

  21. Zhou, X., Zafarani, R., Shu, K., Liu, H.: Fake news: fundamental theories, detection strategies and challenges. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (WSDM ’19). Association for Computing Machinery, New York, pp. 836–837 (2019) https://doi.org/10.1145/3289600.3291382

  22. Kaliyar, R.K., Goswami, A., Narang, P.: FakeBERT: fake news detection in social media with a BERT-based deep learning approach. Multimed Tools Appl. 80, 11765–11788 (2021). https://doi.org/10.1007/s11042-020-10183-2

    Article  Google Scholar 

  23. Sansonetti, G., Gasparetti, F., D’aniello et G., Micarelli, A.: Dtection d’utilisateurs non fiables dans les mdias sociaux : techniques d’apprentissage en profondeur pour la dtection automatique. dans IEEE Access 8, 213154–213167 (2020), https://doi.org/10.1109/ACCESS.2020.3040604

  24. Elyashar, A., Bendahan, J., Puzis, R., Sanmateu, M.-A.: Measurement of online discussion authenticity within online social media. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (ASONAM ’17). Association for Computing Machinery, New York, pp. 627–629. (2017) https://doi.org/10.1145/3110025.3110115

  25. Maurya, S.K., Singh, D.: et Maurya, AK Approches de dtection du spam d’opinion trompeuse: une tude documentaire. Appl. Intell. 53, 2189–2234 (2023). https://doi.org/10.1007/s10489-022-03427-1

  26. Abkenar, S.B., Kashani, M.H., Akbari, M., Mahdipour, E.: Learning textual features for Twitter spam detection: a systematic literature review. Expert Syst. Appl. C (2023). https://doi.org/10.1016/j.eswa.2023.120366

    Article  Google Scholar 

  27. Chakraborty, A., Sundi, J., Satapathy, S.: Spam: A framework for social profile abuse monitoring. Technical report, Department of Computer Science, Stony Brook University, Stony Brook (2012)

  28. Miller, Z., Dickinson, B., Deitrick, W., Hu, W., Wang, A.H.: Twitter spammer detection using data stream clustering. Technical report, Department of Computer Science, Houghton (2014)

  29. Lee, K., Caverlee, J., Webb, S.: Uncovering social spammers: social honeypots+ machine learning. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 435-442. ACM (2010)

  30. Jiang, M., Cui, P., Beutel, A., Faloutsos, C., Yang, S.: Detecting suspicious following behavior in multimillion-node social networks. In: Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, pp. 305–306 (2014)

  31. Gao, T., Yang, J., Peng, W., Jiang, L., Sun et Y., Li, F.: Une mthode base sur le contenu pour la dtection de Sybil dans les rseaux sociaux en ligne via l’apprentissage en profondeur. dans IEEE Access 8, 38753–38766 (2020). https://doi.org/10.1109/ACCESS.2020.2975877

  32. Ekosputra, M.J., Susanto, A., Haryanto et F., Suhartono, D.: Supervised machine learning algorithms to detect instagram fake accounts. In: 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) , pp. 396–400 (2021). https://doi.org/10.1109/ISRITI54043.2021.9702833

  33. Gong, Q., et al.: DeepScan: exploiting deep learning for malicious account detection in location-based social networks. IEEE Commun. Mag. 56(11), 21–27 (2018). https://doi.org/10.1109/MCOM.2018.1700575

    Article  Google Scholar 

  34. Koggalahewa, D., Xu, Y., Foo, E.: An unsupervised method for social network spammer detection based on user information interests. J. Big Data 9, 7 (2022). https://doi.org/10.1186/s40537-021-00552-5

    Article  Google Scholar 

  35. Fazil, M., Abulaish, M.: A hybrid approach for detecting automated spammers in twitter. IEEE Trans. Inf. Forensics Secur. 13(11), 2707–2719 (2018). https://doi.org/10.1109/TIFS.2018.2825958

    Article  Google Scholar 

  36. Ilias, L., Roussaki, I.: Detecting malicious activity in Twitter using deep learning techniques. Appl. Soft Comput. 107, 107360 (2021). https://doi.org/10.1016/j.asoc.2021.107360

  37. Mikolov, T., Chen, K., Akbari, M., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Conference: Proceedings of the International Conference on Learning Representations (2013)

  38. Lee, K., Eoff, B.D., Caverlee, J.: Seven months with the devils: A longterm study of content polluters on twitter. In: Fifth International AAAI Conference on Weblogs and Social Media (2011)

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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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