Machine Learning Algorithms for Spam Detection in Social Networks
Author : K. Nagaramani, K. Vandanarao and B. MamathaVolume 8 No.3 Special Issue:June 2019 pp 41-44
Abstract
Most of the web based social systems like Face book, twitter, other mailing systems and social networks are developed for users to share their information, to interact and engage with the community. Most of the times these social networks will give some troubles to the users by spam messages, threaten messages, hackers and so on.. Many of the researchers worked on this and gave several approaches to detect the spam, hackers and other trouble shoots. In this paper we are discussing some tools to detect the spam messages in social networks. Here we are using RF, SVM, KNN and MLP machine learning algorithms across rapid miner and WEKA. It gives the better results when compared with other tools.
Keywords
Machine Learning, Social Networks, Spam Detection, WEKA and Rapid Miner
Full Text:
References
[1] Kyumin lee, [Online] Available at:https://dl.acm.org/citation.cfm?id=1835522.
[2] Ali Shafiguhaski and k NavidSourati, “Proposed efficient algorithm to filter spam using machine learning techniques”Pacific Science Review A: Natural Science and Engineering, Vol.18, No. 2, pp. 145-149, July 2016.
[3] Hanif, MohamadHazimMd, Adewole, KayodeSakariyah,Anuar, Nor Badrul,Kamsin, and Amirrudin, “Performance Evaluation of Machine Learning Algorithms for Spam Profile Detection on Twitter Using WEKA and RapidMiner”, Advanced Science Letters, Vol. 24, No. 2, pp. 1043-1046, Feb. 2018.
[4] Scott Clayton, Detecting Spam with Azure Machine Learning, 12 Feb 2018.
[5] Parassethi, [Online] Available at: https://ieeexplore.ieee.org/document/8284445/
[6] Sondinh, [Online] Available at: https://www.sciencedirect.com/science/article/pii/S1742287615000079.
[7] M .VictorPrieto, [Online] Available at: https://www.sciencedirect.com/science/article/pii/S016412121300 1684.
[8] Y. Padma, “An automated frame work for document spam detection using enhanced context feature matching”, [Online] Available at:www.ijarcs.info, Vol. 9, No.1,Jan-Feb 2018.
[9] C. Grier, K. Thomas, V. Paxson, and M. Zhang, @spam: the underground on 140 characters or less”, Proceedings of the 17th ACM conference on Computer and communications security, pp. 27-37, 2010.
[10] Z. Chu, S. Gianvecchio, H. Wang and S. Jajodia, “Detecting automation of twitter accounts: Are you a human bot, or cyborg?”, IEEE Transactions on Dependable and Secure Computing, Vol. 9, No. 6, pp.811-824, 2012
[11] F.A. Narudin, A. Feizollah, N.B. Anuar and A. Gani, “Evaluation of machine learning classifiers for mobile malware detection”, Soft Computing, pp. 1-15, 2014.
[12] A.J. Smola and B. Schölkopf, “A tutorial on support vector regression”, Statistics and computing, pp. 199-222, 2004.
[13] L. Noriega, “Multilayer perceptron tutorial”, School of Computing, Staffordshire University, 2005.