Abstract
SMS spams are dramatically increasing year by year because of the expansion of movable users round 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 recently and SMS is taken into account as a trusty and private 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 devices that are connected thereto are considerably vulnerable. Thus there’s a bigger have to be compelled to secure our system from this kind of vulnerability, here network security play a really vital role during this context. In this paper, a SMS spams dataset is taken from UCI Machine Learning repository, and after perform pre-processing and different machine learning techniques such as random forest (RF), Naive Bayes (NB), Support Vector Machine (SVM) are applied to the dataset are applied and 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: Raj, J., Bashar, A., Ramson, S. (eds) Innovative Data Communication Technologies and Application. ICIDCA 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-030-38040-3_69
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DOI: https://doi.org/10.1007/978-3-030-38040-3_69
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