Abstract

Social networks are platforms through which people communicate and share information. Some users commonly known as spammers are misusing these platforms for spreading unsolicited messages commonly known as spam messages. Due to the advancement of internet, it is very difficult to detect spam messages and fake profiles. This research article presents the use of a machine learning algorithm such SVM (Support Vector Machine), which is based on statistical learning methods to detect spam in social networks. This paper also evaluates the classification efficiency of Non Linear SVM using RBS (Radial Basis Function) Kernel.

References

• Agarwal S, Jain. K “Hybrid Approach For Spam Detection using Support Vector Machine and Artificial Immune System”, First International Conference on Network and Soft Computing”, Aug 2014, pg no: 05-09.
• Selamat, Mohammed .M, “ An Evaluation on Efficiency of Hybrid Features for Spam Email Classification”,2015 International Conference on Computer Communication and Control Technology ,April 2015, pg no : 227-231
• “A Hybrid Approach for Spam Filtering using Local Concentration and K- means Clustering”, 2014, 5th International Conference, pg no: 194- 199.
• Salehi, Solmat. A “Hybrid Simple Artificial Immune System and Particle Swam Detection”, “5th Malaysian Conference In Software Engineering”, Aug 2011, pg no: 124-129.
• Chin-Lai, Ming-Chin Tsai, “An Emperical Performance Comparision for Spam Categorization”, 4th International Conference on Hybrid Intelligent Systems, Dec 2014, pg no : 44-48.
• Fabrico Benevenuto, Gabriel Magno, Tiago Rodrigues and Virgilis Almeida, “ Detecting Spammers on Twitter”, CEAS Seventh Annual Collaboration, Electronic Messaging, Anti Abuse and Spam Conference, July 2010.
• M. Mc Cord, M. Chuah, “ Spam Detection On Twitter Using Traditional Classifiers”, ATC,Banff Canada, Sept 2011, pg no: 2-4 IEEE
• Ayon Chakraborty, Jyotinmoy Sundi, Som Satapathy, “SPAM: A Framework For Social Profile Abuse Monitoring”.
• Saber Salehi, Ali Selmat, “Enhanced Genetic Algorithm for Spam Detection in Email”, IEEE 2nd International Conference on Software Engineering and Service Science, July 2011, pg no: 594-597
• Congfu Xu, Baojun Su and Yunbiao Cheng, “An Adaptive Fusion Algorithm For Spam Detection”, IEEE Intelligent System, vol : 29, no: 4, July-Aug 2014, pg no:2-8
• M. Andreolini, A. Bulgarelli, M. Colajanni and F. Mazzoni, “ HoneySpam: Honeypots fighting spam at the source”, in Proc. USENIX Step to Redoducing Unwanted Traffic on the Internet Workshop, Cambridge, March 2005.
• C. Tseng, J.Hvang and M.Chen, “ ProMail: Using Progressive Email Social Network for Spam Detection”, Advances in Knowledge Discovery And Data Mining, LNCS vol: 4426, pg no: 833-840, 2007