Machine Learning Algorithms for Spam Detection in Social Networks
DOI:
https://doi.org/10.51983/ajcst-2019.8.S3.2090Keywords:
Machine Learning, Social Networks, Spam Detection, WEKA and Rapid MinerAbstract
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.
References
Kyumin Lee, [Online]. Available: https://dl.acm.org/citation.cfm?id=1835522.
Ali Shafiguhaski and K. Navid Sourati, "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, Jul. 2016.
Hanif, Mohamad Hazim Md, Adewole, Kayode Sakariyah, 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.
Scott Clayton, "Detecting Spam with Azure Machine Learning," 12 Feb 2018.
Parassethi, [Online]. Available: https://ieeexplore.ieee.org/document/8284445/.
Sondinh, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1742287615000079.
M. Victor Prieto, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0164121213001684.
Y. Padma, "An automated framework for document spam detection using enhanced context feature matching," [Online]. Available: www.ijarcs.info, vol. 9, no. 1, Jan-Feb 2018.
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.
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.
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.
A.J. Smola and B. Schölkopf, "A tutorial on support vector regression," Statistics and Computing, pp. 199-222, 2004.
L. Noriega, "Multilayer perceptron tutorial," School of Computing, Staffordshire University, 2005.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 The Research Publication
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
