Abstract
Rapidly, advancing technology is a double-edged sword as both friend and foe get access to said technology. Spam has become more prevalent than ever with malicious actors using advanced technology to create extremely convincing spam that can lead to major cybersecurity breaches. It has become imperative that we use advanced techniques to combat the proliferation of spam. The objective of our work is to present a systematic overview of the effectiveness of different machine learning and deep learning models integrated with natural language processing concepts. The paper analyzes the different approaches that can be used to identify spam accurately and identifies the most efficient techniques to achieve it. The discussion utilizes a wide range of datasets from email and SMSs to tweets and implements different algorithms like Naïve Bayes, XGBoost, random forest, and convolutional neural networks among others to perform a comprehensive analysis of the best suited methods to achieve high efficiency in identifying spam. It was observed that deep learning models displayed the highest accuracies for spam detection in SMS and emails, while random forest was the most accurate for detecting spam in tweets.
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Anil, A., Sajwan, A., Ramchandar, L., Subhashini, N. (2022). Advanced Spam Detection Using NLP and Deep Learning. In: Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 114. Springer, Singapore. https://doi.org/10.1007/978-981-16-9416-5_23
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