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Detecting Malicious Twitter Bots Using Machine Learning

  • Tanu SatijaEmail author
  • Nirmalya Kar
Conference paper
  • 66 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1192)

Abstract

Cybercrimes and phishing scams have increased multi-folds over the past few years. Now a days, hackers are coming up with new techniques to hack accounts and gain sensitive information about people and organisations. Social networking site like Twitter is one such tool. And due to its large audience hackers use such sites to reach large number of people. They use such sites to circulate malicious URLs, phishing mails etc. which serve as the entry point into the target system. And with the introduction of Twitter Bots, this work got even easier. Twitter bots can send tweets without any human intervention after a fixed regular interval of time. Also their frequency of tweets is much more than humans and therefore they are frequently used by hackers to spread malicious URLs. And due to large number of active members, these malicious URLs are reaching out to more people, therefore increasing the phishing scams and frauds. So this paper proposes a model which will use different algorithms of machine learning, first to detect twitter bots and then find out which of them is posting malicious URLs. In the proposed model, some features have been suggested which distinguishes a twitter bot account from a benign account. Based on those statistical features, model will be trained. The model will help us to filter out the malicious bots which are harmful for legitimate users.

Keywords

Malicious bots Twitter bots Twitter mining Malicious URL 

References

  1. 1.
  2. 2.
  3. 3.
    Mønsted, B., Sapieżyński, P., Ferrara, E., Lehmann, S.: Evidence of complex contagion of information in social media: an experiment using Twitter bots. PLoS ONE 12(9), e0184148 (2017)CrossRefGoogle Scholar
  4. 4.
  5. 5.
  6. 6.
  7. 7.
    Data from Twitter blog. https://blog.twitter.com/
  8. 8.
    Chang, S., Cohen, T., Ostdiek, B.: What is the machine learning? Phys. Rev. D 97(5), 056009 (2018)CrossRefGoogle Scholar
  9. 9.
    Marx, V.: Machine learning, practically speaking. Nat. Meth. 16(6), 463 (2019)CrossRefGoogle Scholar
  10. 10.
    Kan, H.J., Kharrazi, H., Chang, H.-Y., Bodycombe, D., Lemke, K., Weiner, J.P.: Exploring the use of machine learning for risk adjustment: a comparison of standard and penalized linear regression models in predicting health care costs in older adults. PLoS ONE 14(3), e0213258 (2019)CrossRefGoogle Scholar
  11. 11.
    Kuha, J., Mills, C.: On group comparisons with logistic regression models. Sociol. Meth. Res. 47(1), 0049124117747306 (2018)Google Scholar
  12. 12.
    Kainkaryam, S., Ong, C., Sen, S., Sharma, A.: Crowdsourcing salt model building: Kaggle-TGS salt identification challenge. In: 81st EAGE Conference and Exhibition 2019 (2019)Google Scholar
  13. 13.
    Lee, S., Kim, J.: WarningBird: detecting suspicious URLs in Twitter stream. NDSS 12, 1–13 (2012) Google Scholar
  14. 14.
    Chaudhary, M., Hingoliwala, H.A.: Warning Tweet: a detection system for suspicious URLs in Twitter stream. Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET) 2, 297–305 (2014). ISSN: 2321–9653Google Scholar
  15. 15.
    Alshboul, Y., Nepali, R., Wang, Y.: Detecting malicious short URLs on Twitter (2015)Google Scholar
  16. 16.
    Sahoo, D., Liu, C., Hoi, S.C.H.: Malicious URL detection using machine learning: a survey. arXiv preprint arXiv:1701.07179 (2017)
  17. 17.
    Chavoshi, N., Hamooni, H., Mueen, A.: DeBot: Twitter bot detection via warped correlation. In: ICDM, pp. 817–822 (2016)Google Scholar
  18. 18.
    Novotny, J.: Twitter bot detection & categorization-a comparative study of machine learning methods (2019)Google Scholar
  19. 19.
    Davis, C.A., Varol, O., Ferrara, E., Flammini, A., Menczer, F.: BotOrNot: a system to evaluate social bots. In: Proceedings of the 25th International Conference Companion on World Wide Web, pp. 273–274. International World Wide Web Conferences Steering Committee (2016)Google Scholar
  20. 20.
    Chen, Z., Tanash, R.S., Stoll, R., Subramanian, D.: Hunting malicious bots on Twitter: an unsupervised approach. In: Ciampaglia, G.L., Mashhadi, A., Yasseri, T. (eds.) SocInfo 2017. LNCS, vol. 10540, pp. 501–510. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67256-4_40CrossRefGoogle Scholar
  21. 21.
    Devadoss, A.K.V., Thirulokachander, V.R., Devadoss, A.K.V.: Efficient daily news platform generation using natural language processing. Int. J. Inf. Technol. 11(2), 295–311 (2019)Google Scholar
  22. 22.
    Chen, B.-C., Davis, L.S.: Deep representation learning for metadata verification. In: 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW), pp. 73–82. IEEE (2019)Google Scholar
  23. 23.
  24. 24.
    Zeng, J., Liu, M., Xiang, F., Ruiyu, G., Leng, L.: Curvature bag of words model for shape recognition. IEEE Access 7, 57163–57171 (2019)CrossRefGoogle Scholar
  25. 25.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentNIT AgartalaJiraniaIndia

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