Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : G Aishwaryaa | M Chowmiya | J Santhiyaunnamed

Volume 04 Issue 03 2017

ISSN no:  2348-3121

Page no: 108-111

Abstract – Millions of internet users are attracted by a more popular communication and collaboration tools called as Online Social Networks (OSNs). The malicious OSN attackers and spammers used compromised accounts to attack the OSNs. So the compromised accounts were identified by using a set of social behavioral features that characterize the user social activities on OSNs. Then user behavioral feature metrics are combined to devise the user’s social behavioral profile. In the base work, two step processes is carried out to find the compromised accounts, they are calculating Euclidean distance and Euclidean norm. The main drawback is user need to provide threshold to differentiate the legitimate and attacker profiles. To overcome this limitation, we proposed a new approach in which we prepare the training data and classify using the deep neural network algorithm. The deep neural network classified the accounts as normal account and compromised account based on the six introversive and extroversive behavior of OSN users. Finally the experimental results are conducted to show the improvement of proposed methodology in terms of detection error rate and accuracy.

KeywordsOnline Social Network, Compromised Account, Deep Neural Network, User Behavior


  1. Gao, H., Chen, Y., Lee, K., Palsetia, D., & Choudhary, A. N. (2012, February). Towards Online Spam Filtering in Social Networks. In NDSS (Vol. 12, pp. 1-16).
  2. Thomas, K., Grier, C., Ma, J., Paxson, V., & Song, D. (2011, May). Design and evaluation of a real-time url spam filtering service. In Security and Privacy (SP), 2011 IEEE Symposium on (pp. 447-462). IEEE.
  3. Egele, M., Stringhini, G., Kruegel, C., & Vigna, G. (2013, February). Compa: Detecting compromised accounts on social networks. In NDSS.
  4. Cao, Q., Sirivianos, M., Yang, X., & Pregueiro, T. (2012, April). Aiding the detection of fake accounts in large scale social online services. In Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation (pp. 15-15). USENIX Association.
  5. Song, J., Lee, S., & Kim, J. (2011, September). Spam filtering in twitter using sender-receiver relationship. In International Workshop on Recent Advances in Intrusion Detection (pp. 301-317). Springer Berlin Heidelberg.
  6. Egele, M., Stringhini, G., Kruegel, C., & Vigna, G. (2015). Towards detecting compromised accounts on social networks. IEEE Transactions on Dependable and Secure Computing.
  7. Shahabadkar, R., Kamath, M., & Shahabadkar, K. R. (2016, March). Diagnosis of compromised accounts for online social performance profile network. In Wireless Communications, Signal Processing and Networking (WiSPNET), International Conference on (pp. 1552-1557). IEEE.
  8. Lee, K., Caverlee, J., & Webb, S. (2010, July). 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.
  9. Kumar, N., & Reddy, R. N. (2012). Automatic detection of fake profiles in online social networks (Doctoral dissertation).
  10. Wang, A. H. (2010, June). Detecting spam bots in online social networking sites: a machine learning approach. In IFIP Annual Conference on Data and Applications Security and Privacy (pp. 335-342). Springer Berlin Heidelberg.
Scroll Up