Submit your paper : editorIJETjournal@gmail.com Paper Title : An Efficient Spam Detection Technique For IOT devices Using Machine Learning ISSN : 2395-1303 Year of Publication : 2022 10.5281/zenodo.7251491 MLA Style: -D. Shine Rajesh, C. Sindhu, Ch. Nandini, Ch. Rajnandini An Efficient Spam Detection Technique For IOT devices Using Machine Learning , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org APA Style: -D. Shine Rajesh, C. Sindhu, Ch. Nandini, Ch. Rajnandini An Efficient Spam Detection Technique For IOT devices Using Machine Learning , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org Abstract The current use of social media has created incomparable amounts of social data, as it is a cheap and popular information sharing communication platform. Nowadays, a huge percentage of people depend on the accessible material on social networking in their choices. This feature on exchanging knowledge with a wide number of users has quickly prompted social spammers to exploit the network of confidence to distribute spam messages and support personal forums, advertising, phishing, scams and so on. Identifying these spammers and spam material is a hot subject of study, and while large amounts of experiments have recently been conducted to this end, so far the methodologies are only barely able to identify spam feedback, and none of them demonstrates the value of each derived function type. In this study, we have suggested a machine learning- based spam detection system that determines whether or not a specific message in the dataset is spam using a set of machine learning algorithms. Four main features have been used; including user-behavioral, user-linguistic, review-behavioral and review-linguistic, to improve the spam detection process and to gather reliable data. Reference [1]Nurul Fitriah Rusland, Norfaradilla Wahid, Shahreen Kasim, Hanayanti Hafit, “Analysis of Naive Bayes Algorithm for Email Spam Filtering across Multiple Datasets”. [2]J. Rout, S. Singh, S. Jena, and S. Bakshi, “Deceptive Review Detection Using Labeled and Unlabeled Data”. [3]Feng Qian, Abhinav Pathak, Y. Charlie Hu, Z. Morley Mao, and Yinglian Xie, “A Case for Unsupervised-Learning-based Spam Filtering”. [4]Shrawan Kumar Trivedi, “A Study of Machine Learning Classifiers for Spam Detection”. [5]W.A. Awad, S.M. ELseuofi, “Machine Learning Methods for Spam E-mail Classification” [6]S. Gharge, and M. Chavan. An integrated approach for malicious tweets detection using NLP,'' in Proc. Int. Conf. Inventive Communication Computation Technology. (ICICCT), Mar. 2017, pp. 435_438. [7]T. Wu, S. Wen, Y. Xiang, and W. Zhou, ``Twitter spam detection: Survey of new approaches and comparative study,'' Computer Security., vol. 76, pp. 265_284, Jul. 2018. [8]M. Mateen, M. A. Iqbal, M. Aleem, and M. A. Islam, ``A hybrid approach for spam detection for Twitter,'' in Proc. 14th Int. Bhurban Conf. Appl. Sci. Technol. (IBCAST), Jan. 2017, pp. 466_471. [9]F. Fathaliani and M. Bouguessa, ``A model-based approach for identifying spammers in social networks,'' in Proc. IEEE Int. Conf. Data Sci. Adv.Anal. (DSAA), Oct. 2015, pp. 1_9. [10]Saeedreza Shehnepoor, Mostafa Salehi*, Reza Farahbakhsh, Noel Crespi, “NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media “ [11]G. Jain, M. Sharma, and B. Agarwal, ``Spam detection in social media using convolutional and long short term memory neural network,'' Ann. Math. Artif. Intell., vol. 85, no. 1, pp. 21_44, Jan. 2019. [12]C. Meda, F. Bisio, P. Gastaldo, and R. Zunino, ``A machine learning approach for Twitter spammers detection,'' in Proc. Int. Carnahan Conf.Secur. Technol. (ICCST), Oct. 2014, pp. 1_6 Keywords — An Efficient Spam Detection Technique For IOT devices Using Machine Learning |