Abstract
SMS spams are dramatically increasing year by year because of the expansion of movable users around the world. Recent reports have clearly indicated an equivalent. Mobile or SMS spam may be a physical and thriving drawback because of the actual fact that bulk pre-pay SMS packages are handily obtainable and SMS is taken into account as a private and trustable service. SMS spam filtering may be a relatively recent trip to deal such a haul. The amount of information traffic moving over the network is increasing exponentially and therefore the connected devices are considerably vulnerable. Here network security plays a vital role in this context. In this paper, a SMS spams dataset is taken from UCI Machine Learning repository, and after pre-processing, different machine learning techniques such as random forest (RF), Naive Bayes (NB), Support Vector Machine (SVM) are applied to the dataset inorder to compute the performance of these algorithms.
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Goswami, V., Malviya, V., Sharma, P. (2020). Detecting Spam Emails/SMS Using Naive Bayes, Support Vector Machine and Random Forest. In: Pandian, A., Palanisamy, R., Ntalianis, K. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). ICCBI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-030-43192-1_35
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DOI: https://doi.org/10.1007/978-3-030-43192-1_35
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