Novel Approaches to Detect Phony Profile on Online Social Networks (OSNs) Using Machine Learning

Authors

  • Ms Farah Shan  Assistant Professor, Department of Computer Science & Engineering, Maharana Pratap College of Engineering, Kanpur, India
  • Versha Verma  Research Scholar, Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, U.P, India
  • Apoorva Dwivedi  Assistant Professor, Department of Computer Science & Engineering, Invertis University, Bareilly, U.P, India
  • Dr. Yusuf Perwej  Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, U.P, India
  • Ashish Kumar Srivastava  Assistant Professor, Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Lucknow, U.P, India

DOI:

https://doi.org/10.32628/CSEIT23903126

Keywords:

Fake Profile, Twitter, Phony Identities, Detection, Social Network Analysis, Kaggle Dataset, Machine Learning.

Abstract

Currently, almost everyone spends more time on online social media platforms engaging with and exchanging information with people from all over the world, from children to adults. Our lives are greatly influenced by social media sites like Twitter, Facebook, Instagram, and LinkedIn. The social network is evolving into a well-liked platform for connecting with individuals across the globe. Social media platforms exist as a result of the enormous connectivity and information sharing that the internet has made possible. Social media's rising popularity has had both beneficial and detrimental consequences on society. However, it also has to deal with the issue of bogus profiles. False profiles are often constructed by humans, bots, or cyborgs and are used for phishing, propagating rumors, data breaches, and identity theft. Thus, we are emphasizing in this post the significance of setting up a system that can identify false profiles on social media networks. To illustrate the suggested concept of machine learning-based false news identification, we used the Twitter dataset for phony profile detection. The suggested model involves pre-processing to improve the dataset's quality and minimize its dimensions by modifying its contents and features. To forecast the bogus profiles, the widely used machine learning algorithms are used.

References

  1. M. Tsikerdekis and S. Zeadally, "Multiple account identity deception detection in social media using nonverbal behavior", IEEE Transactions on Information Forensics and Security, vol. 9, no. 8, pp. 1311-1321, 2014
  2. Van Der Walt, Estée and Jan Eloff, "Using machine learning to detect fake identities: bots vs humans", IEEE access, vol. 6, pp. 6540-6549, 2018
  3. Adikari and K. Dutta, "Identifying fake profiles in linkedin", PACIS, pp. 278, 2014
  4. Yahao Zhang, Ruimin Hu, Dengshi Li and Xiaochen Wang, Fake Identity Attributes Detection Based on Analysis of Natural and Human Behaviors., vol. 8, pp. 78901-78911, 2020
  5. Yusuf Perwej, Prof. (Dr.) Syed Qamar Abbas, Jai Pratap Dixit, Nikhat Akhtar, Anurag Kumar Jaiswal, “A Systematic Literature Review on the Cyber Security”, International Journal of Scientific Research and Management (IJSRM), ISSN (e): 2321-3418, Volume 9, Issue 12, Pages 669 - 710, 2021, DOI: 10.18535/ijsrm/v9i12.ec04
  6. C. Yang, R. Harkreader and G. Gu, "Empirical evaluation and new design for fighting evolving twitter spammers", IEEE Transactions on Information Forensics and Security, vol. 8, no. 8, pp. 1280-1293, 2013
  7. Asif Perwej, Prof. (Dr.) K. P. Yadav, Prof. (Dr.) Vishal Sood, Dr. Yusuf Perwej, “ An Evolutionary Approach to Bombay Stock Exchange Prediction with Deep Learning Technique”, IOSR Journal of Business and Management (IOSR-JBM), e-ISSN: 2278-487X, p-ISSN: 2319-7668, USA, Volume 20, Issue 12, Ver. V, Pages 63-79, 2018, DOI: 10.9790/487X-2012056379
  8. Yusuf Perwej, “The Bidirectional Long-Short-Term Memory Neural Network based Word Retrieval for Arabic Documents”, Transactions on Machine Learning and Artificial Intelligence (TMLAI), Society for Science and Education, United Kingdom (UK), ISSN 2054-7390, Volume 3, Issue 1, Pages 16 - 27, 2015, DOI: 10.14738/tmlai.31.863
  9. Yusuf Perwej, Firoj Parwej, Mumdouh Mirghani Mohamed Hassan, Nikhat Akhtar, “The Internet-of-Things (IoT) Security: A Technological Perspective and Review”, International Journal of Scientific Research in Computer Science Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, Pages 462-482, 2019. DOI: 10.32628/CSEIT195193
  10. Nikhat Akhtar, Dr. Hemlata Pant, Apoorva Dwivedi, Vivek Jain, Dr. Yusuf Perwej, “A Breast Cancer Diagnosis Framework Based on Machine Learning” , International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN: 2395-1990 , Online ISSN : 2394-4099, Volume 10, Issue 3, Pages 118-132, 2023, DOI: 10.32628/IJSRSET2310375
  11. Venkata K. S. Maddala, Dr. Shantanu Shahi, Dr. Yusuf Perwej, H G Govardhana Reddy, “Machine Learning based IoT application to Improve the Quality and precision in Agricultural System”, European Chemical Bulletin (ECB), ISSN: 2063-5346, SCOPUS, Hungary, Volume 12, Special Issue 6, Pages 1711 – 1722, May 2023, DOI: 10.31838/ecb/2023.12.si6.157
  12. C. Xiao, D. M. Freeman and T. Hwa, "Detecting clusters of fake accounts in onlinesocial networks", Proceedings of the 8th ACM Workshopon Artificial Intelligence and Security, pp. 91-101, 2015
  13. Nikhat Akhtar, Devendera Agarwal, “An Efficient Mining for Recommendation System for Academics”, International Journal of Recent Technology and Engineering (IJRTE), Volume-8, Issue-5, Pages 1619-1626, 2020, DOI: 10.35940/ijrte.E5924.018520
  14. Y. Boshmaf, D. Logothetis, G. Siganos, J. Lería, J. Lorenzo, M. Ripeanu, K. Beznosov, H. Halawa, "Íntegro: Leveraging victim prediction for robust fake account detection in large scale osns", Computers & Security, vol. 61, pp. 142-168, 2016
  15. Asif Perwej, Kashiful Haq, Yusuf Perwej, “Blockchain and its Influence on Market”, International Journal of Computer Science Trends and Technology (IJCST), ISSN 2347 – 8578, Volume 7, Issue 5, Pages 82- 91, 2019, DOI: 10.33144/23478578/IJCST-V7I5P10
  16. M. Al-Qurishi, S. M. M. Rahman, M. S. Hossain, A. Almogren, M. Alrubaian, A. Alamri, M. Al-Rakhami, and B. Gupta, “An efficient key agreement protocol for sybil-precaution in online social networks,” Future Generation Computer Systems, vol. 84, pp. 139–148, Jul. 2018
  17. Chu, Zi, Steven Gianvecchio, Haining Wang, and Sushil Jajodia. “Who is tweeting on Twitter: human, bot, or cyborg?.” In Proceedings of the 26th annual computer security applications conference, pp. 21- 30. ACM, 2010
  18. M. M. Swe and N.N. Myo, “Fake Accounts Classification on Twitter”, International Journal of Latest Engineering and Management Research (IJLEMR), vol 3, no. 6, pp. 141-146, June 2018
  19. K. S. Adewole, T. Han, W. Wu, H. Song, and A. K. Sangaiah, “Twitter spam account detection based on clustering and classification methods,” The Journal of Supercomputing, vol. 76, no. 7, pp. 4802–4837, Oct. 2018
  20. Stringhini, Gianluca, Gang Wang, Manuel Egele, Christopher Kruegel, Giovanni Vigna, Haitao Zheng, and Ben Y. Zhao. “Follow the green: growth and dynamics in twitter follower markets.” In Proceedings of the 2013 conference on Internet measurement conference, pp. 163-176. ACM, 2013
  21. A. Khalil, H. Hajjdiab and N. Al-Qirim, “Detecting Fake Followers in Twitter: A Machine Learning Approach” International Journal of Machine Learning and Computing, vol. 7, no. 6, pp. 198-202, December 2017
  22. S. Khaled, N. El-azi, and H. M. Mokhtar, “Detecting fake accounts on social media”, In: Proc. of IEEE International Conference on Big Data, pp. 3672-3681, 2018
  23. R. R. Rout, G. Lingam, and D. V. L. N. Somayajulu, “Detection of malicious social bots using learning automata with URL features in twitter network,” IEEE Transactions on Computational Social Systems, vol. 7, no. 4, pp. 1004–1018, Aug. 2020
  24. Thomas, Kurt, Damon McCoy, Chris Grier, Alek Kolcz, and Vern Paxson. “Trafficking Fraudulent Accounts: The Role of the Underground Market in Twitter Spam and Abuse.” In Presented as part of the 22nd {USENIX} Security Symposium ({USENIX} Security 13), pp. 195-210, 2013
  25. Xiao C, Freeman DM, Hwa T.,”Detecting clusters of fake accounts in online social networks”, In: Proceedings of the 8th ACM workshop on artifcial intelligence and security, AISec’15. Association for Computing Machinery, New York, NY, USA, pp 91–101, 2015
  26. Shweta Pandey, Rohit Agarwal, Sachin Bhardwaj, Sanjay Kumar Singh, Dr. Yusuf Perwej, Niraj Kumar Singh, “A Review of Current Perspective and Propensity in Reinforcement Learning (RL) in an Orderly Manner”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 9, Issue 1, Pages 206-227, January-February-2023, DOI: 10.32628/CSEIT2390147
  27. M. Mohammadrezaei, M. E. Shiri, and A. M. Rahmani, “Identifying Fake Accounts on Social Networks Based on Graph Analysis and Classification Algorithms”, Security and Communication Networks, pp. 1-8, 2018
  28. M. Conti, R. Poovendran, and M. Secchiero, “Facebook: detecting fake profiles in online social networks”, In: Proc. of the 2012 International Conference on Advances in Social Networks Analysis and Mining, pp. 1071-1078, 2012
  29. Shobhit Kumar Ravi, Shivam Chaturvedi, Dr. Neeta Rastogi, Dr. Nikhat Akhtar, Yusuf Perwej, “A Framework for Voting Behavior Prediction Using Spatial Data”, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), ISSN: 2347-5552, Volume 10, Issue 2, Pages 19-28, 2022, DOI: 10.55524/ijircst.2022.10.2.4
  30. P. S. Rao, J. Gyani, and G. Narsimha, “Fake Profiles Identification in Online Social Networks Using Machine Learning and NLP”, International Journal of Applied Engineering Research, vol. 13, no. 6, pp. 4133-4136, 2018
  31. D. Yuan, Y. Miao, N. Z. Gong, Z. Yang, Q. Li, D. Song, Q. Wang, and X. Liang, “Detecting fake accounts in online social networks at the time of registrations”, In: Proc. of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 1423-1438, 2019
  32. E. V. D. Walt and J. Eloff, “Using Machine Learning to Detect Fake Identities: Bots vs Humans”, IEEE Access, vol. 6, pp. 6540-6549, March 2018
  33. Y. Liu and Y. F. B. Wu, “Fned: a deep network for fake news early detection on social media”, ACM Transactions on Information Systems, Vol. 38, pp. 1-33, 2020
  34. M. Mohammadrezaei, M. E. Shiri, and A. M. Rahmani, “Identifying fake accounts on social networks based on graph analysis and classification algorithms”, Security and Communication Networks, 2018
  35. Firoj Parwej, Nikhat Akhtar, Dr. Yusuf Perwej, “A Close-Up View About Spark in Big Data Jurisdiction”, International Journal of Engineering Research and Application (IJERA), ISSN: 2248-9622, Volume 8, Issue 1, (Part -I1), Pages 26-41, 2018, DOI: 10.9790/9622-0801022641
  36. Liu Y, Ji S, Mittal P (2016) Smartwalk: enhancing social network security via adaptive random walks. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, CCS’16. Association for Computing Machinery, New York, NY, USA, pp 492–503
  37. Yusuf Perwej, Md. Husamuddin, Dr. Majzoob K.Omer, Bedine Kerim, “A Comprehend the Apache Flink in Big Data Environments” , IOSR Journal of Computer Engineering (IOSR-JCE), e-ISSN: 2278-0661, P-ISSN: 2278-8727, USA, Volume 20, Issue 1, Ver. IV, Pages 48-58, 2018 , DOI: 10.9790/0661-2001044858
  38. Jia J, Wang B, Gong NZ Random walk based fake account detection in online social networks. In: 2017 47th annual IEEE/ IFIP international conference on dependable systems and networks (DSN), pp 273–284, 2017
  39. Xiao C, Freeman DM, Hwa T,”Detecting clusters of fake accounts in online social networks”, In: Proceedings of the 8th ACM workshop on artifcial intelligence and security, AISec’15. Association for Computing Machinery, New York, NY, USA, pp 91–101, 2015
  40. Chang AB,” Using machine learning models to detect fake news, bots, and rumors on social media”, Thesis and Dissertations, Arizona State University, Library, 2021
  41. Firoj Parwej, Nikhat Akhtar, Dr. Yusuf Perwej, “An Empirical Analysis of Web of Things (WoT)”, International Journal of Advanced Research in Computer Science (IJARCS), ISSN: 0976-5697, Volume 10, No. 3, Pages 32-40, 2019, DOI: 10.26483/ijarcs.v10i3.6434
  42. A. Al-Sideiri, Z. B. C. Cob, and S. B. M. Drus, Machine Learning Algorithms for Diabetes Prediction: A Review Paper,‖ ACM Int. Conf. Proceeding Ser., pp. 27–32, 2019, doi: 10.1145/3388218.3388231.
  43. Yusuf Perwej, Dr. Ashish Chaturvedi, “Machine Recognition of Hand Written Characters using Neural Networks”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 14, No. 2, Pages 6- 9, 2011, DOI: 10.5120/1819-2380
  44. Dr. E. Baraneetharan, ―Role of Machine Learning Algorithms Intrusion Detection in WSNs: A Survey, ‖ J. Inf. Technol. Digit. World, vol. 02, no. 03, pp. 161– 173, 2020, doi: 10.36548/jitdw.2020.3.004.
  45. Yusuf Perwej, Firoj Parwej, Nikhat Akhtar, “An Intelligent Cardiac Ailment Prediction Using Efficient ROCK Algorithm and K- Means & C4.5 Algorithm”, European Journal of Engineering Research and Science (EJERS), Bruxelles, Belgium, ISSN: 2506-8016 (Online), Vol. 3, No. 12, Pages 126 – 134, 2018, DOI: 10.24018/ejers.2018.3.12.989
  46. Yusuf Perwej, Firoj Parwej, “A Neuroplasticity (Brain Plasticity) Approach to Use in Artificial Neural Network”, International Journal of Scientific & Engineering Research (IJSER), France , ISSN 2229 – 5518, Volume 3, Issue 6, Pages 1- 9, 2012, DOI: 10.13140/2.1.1693.2808
  47. A. Telikani, A. Tahmassebi, W. Banzhaf, and A. H. Gandomi, Evolutionary Machine Learning: A Survey,‖ ACM Comput. Surv., vol. 54, no. 8, 2022
  48. R. Katarya and S. Jain, Comparison of different machine learning models for diabetes detection,‖ Proc. 2020 IEEE Int. Conf. Adv. Dev. Electr. Electron. Eng. ICADEE 2020, no. Icadee, pp. 0–4, 2020
  49. Nikhat Akhtar, Devendera Agarwal, “An Efficient Mining for Recommendation System for Academics”, International Journal of Recent Technology and Engineering (IJRTE), ISSN 2277-3878 (online), SCOPUS, Volume-8, Issue-5, Pages 1619-1626, 2020, DOI: 10.35940/ijrte.E5924.018520
  50. K. Kersting, N. M. Kriege, C. Morris, P. Mutzel and M. Neumann, "Benchmark data sets for graph kernels", 2016
  51. Y. Guo, X. Cao, W. Zhang and R. Wang, "Fake colorized image detection", IEEE Transactions on Information Forensics and Security, vol. 73, no. 8, pp. 1932-1944, 2018
  52. https://www.kaggle.com/datasets/mtesconi/twitter-deep-fake-text
  53. M. Egele, G. Stringhini, C. Kruegel and G. Vigna, "Towards detecting compromised accounts on social networks", IEEE Trans. Dependable Secure Comput., vol. 14, no. 4, pp. 447-460, Jul./Aug. 2017
  54. A. El Azab, A. M. Idrees, M. A. Mahmoud and H. Hefny, "Fake account detection in twitter based on minimum weighted feature set", Int. Scholarly Sci. Res. Innov., vol. 10, no. 1, pp. 13-18, 2016
  55. Yusuf Perwej, Dr. Shaikh Abdul Hannan, Firoj Parwej, Nikhat Akhtar, “A Posteriori Perusal of Mobile Computing”, International Journal of Computer Applications Technology and Research (IJCATR), ATS (Association of Technology and Science), India, ISSN 2319–8656 (Online), Volume 3, Issue 9, Pages 569 - 578, 2014, DOI: 10.7753/IJCATR0309.1008
  56. Asif Perwej, Prof. (Dr.) K. P. Yadav, Prof. (Dr.) Vishal Sood, Yusuf Perwej, “ An Evolutionary Approach to Bombay Stock Exchange Prediction with Deep Learning Technique”, IOSR Journal of Business and Management (IOSR-JBM), e-ISSN: 2278-487X, p-ISSN: 2319-7668, USA, Volume 20, Issue 12, Ver. V, Pages 63-79, December. 2018, DOI: 10.9790/487X-2012056379
  57. Prof. Kameswara Rao Poranki, Dr. Yusuf Perwej, Dr. Asif Perwej,” The Level of Customer Satisfaction related to GSM in India “, TIJ's Research Journal of Science & IT Management – RJSITM, International Journal's-Research Journal of Science & IT Management of Singapore, ISSN: 2251-1563, Singapore, in www.theinternationaljournal.org as RJSSM, Volume 04, Number: 03, Pages 29-36 , 2015
  58. H. Zhao, H. Zhou, C. Yuan, Y. Huang and J. Chen, "Social discovery: Exploring the correlation among three-dimensional social relationships", IEEE Trans. Comput. Social Syst., vol. 2, no. 3, pp. 77-87, Sep. 2015
  59. C. Chen, Y. Wang, J. Zhang, Y. Xiang, W. Zhou and G. Min, "Statistical features-based real-time detection of drifted twitter spam", IEEE Trans. Inf. Forensics Secur., vol. 12, no. 4, pp. 914-925, Apr. 2016
  60. B. Ersahin, O. Aktas, D. Kilinc and C. Akyol, "Twitter fake account detection", Proc. IEEE Int. Conf. Comput. Sci. Eng., pp. 388-392, 2017
  61. Y. Perwej, Nikhat Akhtar, Firoj Parwej, “The Kingdom of Saudi Arabia Vehicle License Plate Recognition using Learning Vector Quantization Artificial Neural Network”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 98, No.11, Pages 32 – 38, July 2014, DOI: 10.5120/17230-7556
  62. G. Suarez-Tangil, M. Edwards, C. Peersman, G. Stringhini, A. Rashid and M. Whitty, "Automatically dismantling online dating fraud", IEEE Trans. Inf. Forensics Secur., vol. 15, pp. 1128-1137, 2020

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2023-06-30

Issue

Volume 9, Issue 3, May-June-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ms Farah Shan, Versha Verma, Apoorva Dwivedi, Dr. Yusuf Perwej, Ashish Kumar Srivastava, " Novel Approaches to Detect Phony Profile on Online Social Networks (OSNs) Using Machine Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 3, pp.555-568, May-June-2023. Available at doi : https://doi.org/10.32628/CSEIT23903126