Skip to main content
SpringerLink
Log in

IntelliTweet: A Multifaceted Feature Approach to Detect Malicious Tweets

  • Conference paper
  • First Online:
Foundations and Practice of Security (FPS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14551))

Included in the following conference series:

  • 61 Accesses

Abstract

Twitter faces an ongoing issue with malicious tweets from deceptive accounts engaged in phishing, scams, and spam, negatively impacting the overall Twitter user experience. In response to growing security concerns, various machine learning-based methods have been deployed to detect and analyze these malicious activities. However, the evolving nature of the threats and tactics used by malicious actors cast doubts on the effectiveness of previously employed techniques. These methods often encounter challenges in addressing URL obfuscation techniques and managing false positive predictions. In this paper, we present “IntelliTweet”, an innovative solution designed to comprehend tweet content and accurately identify malicious tweets. This is achieved by incorporating a combination of contextual and content-based features, surpassing the use of conventional features alone. IntelliTweet takes a holistic approach that includes URL analysis, sentiment analysis, Twitter user analysis, and TFIDF-based content analysis, all working in tandem to enhance malicious tweet detection. For this system, our evaluation strategy places emphasis on reducing false positives while maintaining high precision. Through comparative experiments, we have demonstrated that IntelliTweet effectively counters URL obfuscation techniques, is robust, and minimizes the false positive rate. The system achieved a 98.38% precision, a 97.54% f-measure, and yielded a false positive rate of 0.14.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aggarwal, A., Rajadesingan, A., Kumaraguru, P.: Phishari: automatic realtime phishing detection on twitter. In: 2012 eCrime Researchers Summit, pp. 1–12 (2012). https://doi.org/10.1109/eCrime.2012.6489521

  2. Alom, Z., Carminati, B., Ferrari, E.: A deep learning model for twitter spam detection. Online Soc. Netw. Media 18, 100079 (2020). https://doi.org/10.1016/j.osnem.2020.100079

    Article  Google Scholar 

  3. Azeez, N.A., Misra, S., Margaret, I.A., Fernandez-Sanz, L., et al.: Adopting automated whitelist approach for detecting phishing attacks. Comput. Sec. 108, 102328 (2021)

    Article  Google Scholar 

  4. Bell, S., Paterson, K., Cavallaro, L.: Catch me (on time) if you can: understanding the effectiveness of twitter url blacklists. arXiv preprint arXiv:1912.02520 (2019)

  5. Bouijij, H., Berqia, A.: Machine learning algorithms evaluation for phishing urls classification. In: 2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT), pp. 01–05 (2021). https://doi.org/10.1109/ISAECT53699.2021.9668489

  6. Cao, J., Li, Q., Ji, Y., He, Y., Guo, D.: Detection of forwarding-based malicious urls in online social networks. Int. J. Parallel Prog. 44, 163–180 (2016)

    Article  Google Scholar 

  7. Casanove, O.d., Sèdes, F.: Malicious human behaviour in information system security: contribution to a threat model for event detection algorithms. In: Foundations and Practice of Security, pp. 208–220. Springer Nature Switzerland, Cham (2023)

    Google Scholar 

  8. Chen, C., et al.: Investigating the deceptive information in twitter spam. Futur. Gener. Comput. Syst. 72, 319–326 (2017). https://doi.org/10.1016/j.future.2016.05.036

    Article  Google Scholar 

  9. Chen, C., Zhang, J., Chen, X., Xiang, Y., Zhou, W.: 6 million spam tweets: a large ground truth for timely twitter spam detection. In: 2015 IEEE International Conference on Communications (ICC), pp. 7065–7070 (2015). https://doi.org/10.1109/ICC.2015.7249453

  10. Choi, D., Han, J., Chun, S., Rappos, E., Robert, S., Kwon, T.T.: Bit.ly/practice: uncovering content publishing and sharing through url shortening services. Telematics and Informatics 35(5), 1310–1323 (2018). https://doi.org/10.1016/j.tele.2018.03.003

  11. Concone, F., Re, G.L., Morana, M., Ruocco, C.: Assisted labeling for spam account detection on twitter. In: 2019 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 359–366. IEEE (2019)

    Google Scholar 

  12. Djaballah, K.A., Boukhalfa, K., Ghalem, Z., Boukerma, O.: A new approach for the detection and analysis of phishing in social networks: the case of twitter. In: 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2020). https://doi.org/10.1109/SNAMS52053.2020.9336572

  13. Dzeha, Eric Edem, J., Guy-Vincent: eric-edem/The_intellitweet: A Multifaceted Feature Approach to Detect Malicious Tweets. https://github.com/eric-edem/The_IntelliTweet

  14. Gangwar, S.S., Rathore, S.S., Chouhan, S.S., Soni, S.: Predictive modeling for suspicious content identification on twitter. Soc. Netw. Anal. Min. 12(1), 149 (2022)

    Article  Google Scholar 

  15. Gheewala, S., Patel, R.: Machine learning based twitter spam account detection: a review. In: 2018 Second International Conference on Computing Methodologies and Communication (ICCMC), pp. 79–84 (Feb 2018). https://doi.org/10.1109/ICCMC.2018.8487992

  16. Hong, J., Kim, T., Liu, J., Park, N., Kim, S.W.: Phishing url detection with lexical features and blacklisted domains. Adaptive Autonom. Sec. Cyber Syst. 253–267 (2020)

    Google Scholar 

  17. Horawalavithana, S., De Silva, R., Nabeel, M., Elvitigala, C., Wijesekara, P., Iamnitchi, A.: Malicious and Low Credibility URLs on Twitter during the AstraZeneca COVID-19 Vaccine Development, arXiv:2102.12223 (Feb 2021), [cs] version: 1

  18. Inuwa-Dutse, I., Liptrott, M., Korkontzelos, I.: Detection of spam-posting accounts on twitter. Neurocomputing 315, 496–511 (2018)

    Article  Google Scholar 

  19. Jabardi, M., Hadi, A.S.: Twitter fake account detection and classification using ontological engineering and semantic web rule language. Karbala Inter. J. Mod. Sci. 6(4), 8 (2020)

    Article  Google Scholar 

  20. Jain, A.K., Gupta, B.: A survey of phishing attack techniques, defence mechanisms and open research challenges. Enterprise Inform. Syst. 16(4), 527–565 (2022)

    Article  Google Scholar 

  21. Jain, A.K., Gupta, B.B.: Towards detection of phishing websites on client-side using machine learning based approach. Telecommun. Syst. 68, 687–700 (2018)

    Article  Google Scholar 

  22. Karami, A., Lundy, M., Webb, F., Dwivedi, Y.K.: Twitter and research: a systematic literature review through text mining. IEEE Access 8, 67698–67717 (2020). https://doi.org/10.1109/ACCESS.2020.2983656

    Article  Google Scholar 

  23. Khonji, M., Iraqi, Y., Jones, A.: Phishing detection: a literature survey. IEEE Commun. Surv. Tutorials 15(4), 2091–2121 (2013). https://doi.org/10.1109/SURV.2013.032213.00009

    Article  Google Scholar 

  24. Korkmaz, M., Sahingoz, O.K., Diri, B.: Detection of phishing websites by using machine learning-based url analysis. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–7 (2020). https://doi.org/10.1109/ICCCNT49239.2020.9225561

  25. Madisetty, S., Desarkar, M.S.: A neural network-based ensemble approach for spam detection in twitter. IEEE Trans. Comput. Soc. Syst. 5(4), 973–984 (2018). https://doi.org/10.1109/TCSS.2018.2878852

    Article  Google Scholar 

  26. Marchal, S., Saari, K., Singh, N., Asokan, N.: Know your phish: novel techniques for detecting phishing sites and their targets. In: 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), pp. 323–333 (2016). https://doi.org/10.1109/ICDCS.2016.10

  27. Mohammad, R., McCluskey, L.: Uci machine learning repository. https://archive.ics.uci.edu/dataset/327/phishing+websites

  28. Nakano, H., et al.: Canary in twitter mine: collecting phishing reports from experts and non-experts. arXiv preprint arXiv:2303.15847 (2023)

  29. Nguyen, D.Q., Vu, T., Nguyen, A.T.: Bertweet: a pre-trained language model for english tweets. arXiv preprint arXiv:2005.10200 (2020)

  30. Rao, R.S., Vaishnavi, T., Pais, A.R.: Catchphish: detection of phishing websites by inspecting urls. J. Ambient. Intell. Humaniz. Comput. 11, 813–825 (2020)

    Article  Google Scholar 

  31. Rodrigues, A.P., Fernandes, R., Shetty, A., Lakshmanna, K., Shafi, R.M., et al.: Real-time twitter spam detection and sentiment analysis using machine learning and deep learning techniques. Comput. Intell. Neurosci. (2022)

    Google Scholar 

  32. Rout, R.R., Lingam, G., Somayajulu, D.V.L.N.: Detection of malicious social bots using learning automata with url features in twitter network. IEEE Trans. Comput. Soc. Syst. 7(4), 1004–1018 (2020). https://doi.org/10.1109/TCSS.2020.2992223

    Article  Google Scholar 

  33. Roy, S.S., Karanjit, U., Nilizadeh, S.: Evaluating the effectiveness of phishing reports on twitter. In: 2021 APWG Symposium on Electronic Crime Research (eCrime), pp. 1–13 (2021). https://doi.org/10.1109/eCrime54498.2021.9738786

  34. Sameen, M., Han, K., Hwang, S.O.: Phishhaven-an efficient real-time ai phishing urls detection system. IEEE Access 8, 83425–83443 (2020). https://doi.org/10.1109/ACCESS.2020.2991403

    Article  Google Scholar 

  35. Sharma, N., Sharma, N., Tiwari, V., Chahar, S., Maheshwari, S., et al.: Real-time detection of phishing tweets. In: Fourth International Conference on Computer Science Engineering Application, pp. 215–27 (2014)

    Google Scholar 

  36. Tang, S., Mi, X., Li, Y., Wang, X., Chen, K.: Clues in tweets: twitter-guided discovery and analysis of sms spam. In: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, pp. 2751–2764 (2022)

    Google Scholar 

  37. Twitter: About unsafe links. https://help.twitter.com/en/safety-and-security/phishing-spam-and-malware-links

  38. Twitter: Twitter API Documentation. https://developer.twitter.com/en/docs/twitter-api

  39. VirusTotal: Home. https://www.virustotal.com/gui/home/upload

  40. Wani, K., Patil, A., Mukherjee, S., Sarkar, S.: Malicious twitter bot detector. In: 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE), pp. 1–6 (2021). https://doi.org/10.1109/ICNTE51185.2021.9487674

  41. Wikipedia: https://en.wikipedia.org/wiki/Anti-Phishing_Working_Group

  42. Zhang, Y., Hong, J.I., Cranor, L.F.: Cantina: a content-based approach to detecting phishing web sites. In: Proceedings of the 16th International Conference on World Wide Web, pp. 639–648 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science, University of Ottawa, Ottawa, Canada

    Eric Edem Dzeha & Guy-Vincent Jourdan

Authors
  1. Eric Edem Dzeha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guy-Vincent Jourdan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eric Edem Dzeha .

Editor information

Editors and Affiliations

  1. University of Bordeaux, Bordeaux, France

    Mohamed Mosbah

  2. Toulouse III - Paul Sabatier University, Toulouse, France

    Florence Sèdes

  3. Université Laval, Québec, QC, Canada

    Nadia Tawbi

  4. University of Bordeaux, Bordeaux, France

    Toufik Ahmed

  5. Polytechnique Montréal, Montreal, QC, Canada

    Nora Boulahia-Cuppens

  6. Telecom SudParis, Palaiseau, France

    Joaquin Garcia-Alfaro

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dzeha, E.E., Jourdan, GV. (2024). IntelliTweet: A Multifaceted Feature Approach to Detect Malicious Tweets. In: Mosbah, M., Sèdes, F., Tawbi, N., Ahmed, T., Boulahia-Cuppens, N., Garcia-Alfaro, J. (eds) Foundations and Practice of Security. FPS 2023. Lecture Notes in Computer Science, vol 14551. Springer, Cham. https://doi.org/10.1007/978-3-031-57537-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-57537-2_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57536-5

  • Online ISBN: 978-3-031-57537-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation