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Comparison of machine learning techniques for spam detection

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Abstract

Email is a useful communication medium for better reach. There are two types of emails, those are ham or legitimate email and spam email. Spam is a kind of bulk or unsolicited email that contains an advertisement, phishing website link, malware, Trojan, etc. This research aims to classify spam emails using machine learning classifiers and evaluate the performance of classifiers. In the pre-processing step, the dataset has been analyzed in terms of attributes and instances. In the next step, thirteen machine learning classifiers are implemented for performing classification. Those classifiers are Adaptive Booster, Artificial Neural Network, Bootstrap Aggregating, Decision Table, Decision Tree, J48, K-Nearest Neighbor, Linear Regression, Logistic Regression, Naïve Bayes, Random Forest, Sequential Minimal Optimization and, Support Vector Machine. In terms of accuracy, the Random Forest classifier performs best and the performance of the Naïve Bayes classifier is substandard compared to the rest of the classifiers. Random Forest classifier had the accuracy of 99.91% and 99.93% for the Spam Corpus and Spambase datasets respectively. The naïve Bayes classifier had the accuracy of 87.63% and 79.53% for the Spam Corpus and Spambase datasets respectively.

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Funding

This research work has been written with the financial support of Rashtriya Uchchatar Shiksha Abhiyan (RUSA- Phase 2.0) grant sanctioned vide Letter No. F.24–51/2014-U, Policy (TNMulti-Gen), Dept. of Edn. Govt. of India, Dt. 09.10.2018.

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    Argha Ghosh & A. Senthilrajan

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Ghosh, A., Senthilrajan, A. Comparison of machine learning techniques for spam detection. Multimed Tools Appl 82, 29227–29254 (2023). https://doi.org/10.1007/s11042-023-14689-3

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