SPADES: Scalable and Privacy Assured Detection of Spams
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AbdelrahmanAlMahmoud, Member, IEEE, Ernesto Damiani, Senior Member, IEEE, HadiOtrok, Senior Member, IEEE, YousofAl-Hammadi, Member, IEEE . Spamdoop: A privacy-preserving Big Data platform for collaborative spam detection pages 1-8 , IEEE2017 [2] Kaspersky Lab, “Spam and Phishing Statistics ReportQ1-2014”. [3] Kaspersky Lab, “Spam and Phishing Statistics for2016”. [4] J. Francois, S. Wang, W. Bronzi, R.tate, and T. Engel. Bot-cloud: Detecting botnets using mapreduce. In IEEE International Workshop on Information Forensics and Security (WIFS), pages1–6. IEEE,2011 [5] Jesse Kornblum, Digital Forensic Research Conference , Identifying almost identiical files using Context Triggered PiecewiseHashing [6] N. Spirin and J. Han. Survey on web spam detection: principles and algorithms. ACM SIGKDD explorations Newsletter, 1.l.13: pages 50–64,2012. [7] W. Shi and M. Xie. A reputation- based collaborative approach for spam filtering. Conference on Parallel
and Distributed Computing Systems, AASRI rocedia, vol. 5: pages 220– 227,2013. [8] M. Sirivianos, K. Kim, and X. Yang. Socialfilter: Introducing social trust to collaborative spam mitigation. In INFOCOM,pages2300–2308. IEEE, 2011 [9] G. Caruana and M. Li. A survey of emerging approaches to spam filtering. ACM Computing Surveys, vol. 44: pages 1–27,2012. [10] C. Karlberger, G. Bayler, C. Kruegel, and E. Kirda. Exploiting redundancy in natural language to penetrate bayesian spam filters.pages1–7,2007.
DOI: https://doi.org/10.26483/ijarcs.v9i0.6253
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