A survey on : Online Social Networking Attacks Detection Techniques

Authors

  • Ankita Sharma  IEEE Member, Chandigarh University, Punjab, India

DOI:

https://doi.org//10.32628/CSEIT21732

Keywords:

Online social Networking, vulnerabilities , Attacks , Security Threats .

Abstract

Today's due the popularity of internet number of users are increase on every social media platform. In recent research found that 80% of youth depend on social media to make new friends , share photos. Through this they get popularity and large number of user base and become influencers . Most of the social media platform are providing different privacy and security . Still attacker find out the way to breech the security, privacy and confidently of users and companies or organizations using several techniques . This paper highlight the major security issues phasing by many social networking web applications. Also identify the solution based on attacks in different literature . At last, we discuss open research issues

References

  1. D.Boyd and N.B.Ellison,”Socil Networks Sites:Definition,History and Scholarship”,Computer-Mediated commun.,vol.no:13.2007.
  2. Wu-Chen su.”Integrating and mining virtual communities across multiple online social networks:concepts,approaches and challenges”,IEEE,2014
  3. Laura Marcia Villalba Monné.”A Survey of Mobile Social Networking”.international journal of scientific engineering,2014
  4. Kefi, H., and C. Perez. 2018. "Dark Side of Online Social Networks: Technical, Managerial, and Behavioral Perspectives." Encyclopedia of Social Network Analysis and Mining 1–22.
  5. Boshmaf, Y., I. Muslukhov, K. Beznosov, and M. Ripeanu. 2011, December. “The Socialbot Network: When Bots Socialize for Fame and Money.” Proceedings of the 27th annual Computer Security Applications Conference, Orlando, FL (pp. 93–102). ACM
  6. Twitter. https://twitter.com/
  7. Facebook. 2018. “Facebook Security Products: Protect Your Computer with Free Security Software Downloads from Your Friends at Facebook.” https://www.facebook.com/security/app_ 360406100715618
  8. Han j,”Mining heterogeneous information networks:the next frontier”,proceedings of 18th ACM SIGKDD international conference on Knowledge discovery and data mining,ACM,China pp. 2-3,2012.
  9. D. Boyd. Social Network Sites: Public, Private, or What? http://kt.flexiblelearning.net.au/tkt2007/edition13/social-networksites
  10. Sahoo, S. R., and B. B. Gupta. 2018. Security Issues and Challenges in Online Social Networks (Osns) Based on User Perspective. Computer and Cyber Security: Principles, Algorithm, Applications, and Perspectives, 591–606. UNited Kingdom: CRC Press.
  11. Statista report about online social networking users. https://www.statista.com/statistics/278414/ number-of-worldwide-social-network-users.
  12. Beato, F., M. Conti, and B. Preneel. 2013. “Friend in the Middle (Fim): Tackling De-Anonymization in Social Networks.” 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), San Diego, CA, March (pp. 279–284).
  13. Tian, Y., J. Yuan, and S. Yu. 2016. “SBPA: Social Behavior Based Cross Social Network Phishing Attacks.” 2016 IEEE Conference on Communications and Network Security (CNS), Philadelphia, PA, October (pp. 366–367)IEEE.
  14. Fire, M., G. Katz, and Y. Elovici. 2012. “Strangers Intrusion Detection-Detecting Spammers and Fake Profiles in Social Networks Based on Topology Anomalies.” Human Journal 1 (1): 26–39
  15. M. Fire, G. Katz, and Y. Elovici, “Strangers intrusion detection-detecting spammers and fake profiles in social networks based on topology anomalies,”Human J., vol. 1, no. 1, pp. 26–39, 2012
  16. Baltazar, J., J. Costoya, and R. Flores. 2009. “The Real Face of Koobface: The Largest Web 2.0 Botnet Explained.” Trend Micro Research 5 (9): 10.
  17. Yang, T., Y. Yang, K. Qian, D. C. T. Lo, Y. Qian, and L. Tao. 2015. “Automated Detection and Analysis for Android Ransomware.” 2015 IEEE 17th International Conference on High Performance Computing and Communications (HPCC), 2015 IEEE 7th International Symposium on Cyberspace Safety and Security (CSS), 2015 IEEE 12th International Conferen on Embedded Software and Systems (ICESS), Melbourne, August (pp. 1338–1343). IEEE.
  18. Bilge, L., T. Strufe, D. Balzarotti, and E. Kirda. 2009. “All Your Contacts are Belong to Us: Automated Identity Theft Attacks on Social Networks.” Proceedings of the 18th International Conference on World Wide Web, Madrid, April (pp. 551–560). ACM.
  19. Williams, E. J., J. Hinds, and A. N. Joinson. 2018. “Exploring Susceptibility to Phishing in the Workplace.” International Journal of Human-Computer Studies 120: 1–13. doi:10.1016/j. ijhcs.2018.06.004
  20. Humphreys, L. 2007. “Mobile Social Networks and Social Practice: A Case Study of Dodgeball.” Journal of Computer-Mediated Communication 13 (1): 341–360. doi:10.1111/j.1083- 6101.2007.00399.x
  21. Alghamdi, B., J. Watson, and Y. Xu. 2016. “Toward Detecting Malicious Links in Online Social Networks through User Behavior.” 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW), Omaha, NE, October (pp. 5–8). IEEE
  22. Ubaid Ur Rehman, Waqas Ahmad Khan, Nazar Abbas Saqib, Muhammad Kaleem,” On Detection and Prevention of Clickjacking Attack for OSNs”,IEEE, 11th International Conference on Frontiers of Information Technology,2013.
  23. inxue Zhang, Rui Zhang, Yanchao Zhang, and Guanhua Yan,” On the Impact of Social Botnets for Spam Distribution and Digital-influence Manipulation”, IEEE Conference on Communications and Network Security (CNS),2013.
  24. Taiki Oosawa,Takeshi Matsuda,”SQL injection attack detection method using the approximation function of zeta distribution” ,IEEE International conference,2014.
  25. Martin, M., M. S. Lam, “Automatic Generation of XSS and SQL Injection Attacks with GoalDirected Model Checking,” 17th Conference on Security Symposium,2008.
  26. Liu, B., Z. Ni, J. Luo, J. Cao, X. Ni, B. Liu, and X. Fu. 2018. “Analysis of and Defense against Crowd-Retweeting Based Spam in Social Networks.”
  27. Gong, Q., Y. Chen, X. He, Z. Zhuang, T. Wang, H. Huang, . . . X. Fu. 2018. “DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks.” IEEE Communications Magazine, Feature Topic on Mobile Big Data for Urban Analytics 56 (11): 21–27
  28. Yeh, L. Y., Y. L. Huang, A. D. Joseph, S. W. Shieh, and W. J. Tsaur. 2012. “A Batch-Authenticated and Key Agreement Framework for P2p-Based Online Social Networks.” IEEE Transactions on Vehicular Technology 61 (4): 1907–1924.
  29. Liang, H., Z. Chen, and J. Wu. 2018. “Dynamic Reputation Information Propagation Based Malicious Account Detection in OSNs.” Wireless Networks 1–14
  30. Campos, G. F., G. M. Tavares, R. A. Igawa, and R. C. Guido. 2018. “Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets.” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14 (1s): 26.
  31. Tanmay S. Mule , Aakash S. Mahajan, Sangharatna Kamble, Omkar Khatavkar,”Intrusion Protection against SQL Ijection and cross-site scripting attacks using a reverse proxy”, IJCSIT, Vol. 5 (3),2014.

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

Downloads

Published

2021-06-30

Issue

Volume 7, Issue 3, May-June-2021

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]
Ankita Sharma, " A survey on : Online Social Networking Attacks Detection Techniques, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.44-50, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT21732