Email this article Twitter Spam Detection Using Hybrid Method
Abstract
We present a half breed approach for perceiving automated spammers by amalgamating system based features with other component classes, to be explicit metadata-, content-, and affiliation based features. The peculiarity of the proposed approach lies in the portrayal of customers subject to their relationship with their disciples given that a customer can evade incorporates that are related to his/her very own activities, anyway avoiding those reliant on the enthusiasts is problematic. Nineteen special features, including six as of late portrayed features and two renamed features, are perceived for learning three classifiers, specifically, self-assertive boondocks, decision tree, and Bayesian framework, on a veritable dataset that contains charitable customers and spammers. The partition force of different component classes is furthermore analyzed, and association and system based features are made plans to be the best for spam ID, however metadata-based highlights are demonstrated to be the least powerful.
References
M. Tsikerdekis, “Identity deception prevention using common contribution network data,” IEEE Trans. Inf. Forensics Security, vol. 12, no. 1,pp. 188–199, Jan. 2017.
T. Anwar and M. Abulaish, “Ranking radically influential Web forum users,” IEEE Trans. Inf. Forensics Security, vol. 10, no. 6,pp. 1289–1298, Jun. 2015.
Y. Boshmaf, I. Muslukhov, K. Beznosov, and M. Ripeanu, “Design and analysis of social botnet,” Comput. Netw., vol. 57, no. 2, pp. 556–578,2013.
D. Fletcher, “A brief history of spam,” TIME, Nov. 2, 2009.[Online]. Available:http://www.time.com/time/business/article/0,8599,1933796,00.html
Y. Boshmaf, M. Ripeanu, K. Beznosov, and E. Santos-Neto, “Thwarting fake OSN accounts by predicting their victims,” in Proc. AISec, Denver,CO, USA, 2015, pp. 81–89.
A. A. Amleshwaram, N. Reddy, S. Yadav, G. Gu, andC. Yang, “CATS: Characterizing automation of Twitter spammers,” in Proc. COMSNETS, Bengaluru, India, Jan. 2013, pp. 1–10.
K. Lee, J. C. Lee, and S. Webb, “Uncovering social spammers: Socialhoneypots + machine learning,” in Proc. SIGIR, Geneva, Switzerland,Jul. 2010, pp. 435–442.
G. Stringhini, C. Kruegel, and G. Vigna, “Detecting spammers on socialnetworks,” in Proc. ACSAC, Austin, TX, USA, 2010, pp. 1–9.
H. Yu, M. Kaminsky, P. B. Gibbons, and A. D. Flaxman, “SybilGuard:Defending against sybil attacks via social networks,” IEEE/ACM Trans.Netw., vol. 16, no. 3, pp. 576–589, Jun. 2008.
H. Gao, J. Hu, C. Wilson, Z. Li, Y. Chen, and B. Y. Zhao, “Detectingand characterizing social spam campaigns,” in Proc. IMC, Melbourne,VIC, Australia, 2001, pp. 35–47.
W. Wei, F. Xu, C. C. Tan, and Q. Li “Sybildefender: Defend against sybil attacks in large social networks,” in Proc. INFOCOM, Orlando, FL, USA, Mar. 2012, pp. 1951–1959.
C. Yang, R. C. Harkreader, and G. Gu, “Die free or live hard? Empirical evaluation and new design for fighting evolving Twitter spammers,” in Proc. RAID, Menlo Park, CA, USA, 2011, pp. 318–337.
S. Lee and J. Kim, “WarningBird: A near real-time detection system forsuspicious URLs in Twitter stream,” IEEE Trans. Depend. Sec. Comput.,vol. 10, no. 3, pp. 183–195, May 2013.
M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz, “A Bayesianapproach to filtering junk e-mail,” in Proc. Workshop Learn. Text Categorization, Madison, WI, USA, 1998, pp. 98–105.
C. Schäfer, “Detection of compromised email accounts used by a spam botnet with country counting and theoretical geographical travelling speed extracted from metadata,” in Proc. ISSREW, Naples, Italy,Nov. 2014, pp. 329–334.
Refbacks
- There are currently no refbacks.
Copyright © 2013, All rights reserved.| ijseat.com
International Journal of Science Engineering and Advance Technology is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJSEat , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.