Abstract
The major issue with the email spam is a major issue nowadays, as Internet users grow rapidly. The attacker, or the spammer, mainly used it for phishing or to commit fraud by sending malicious links in spam emails that could harm our system. It is fair to say that, as email communication has increased in recent years in this Internet age, owing to its low cost and simplicity in use for messaging and sharing important information with others, spamming has become a significant problem, with email spam being the most widely recognized form of the problem. Spam emails have a financial impact on organizations, but they also irritate individual email users and frequently cause inconvenient behavior. In this paper, we have designed an intelligent system for the identification of spam or ham email via the use of several machine-learning algorithms. Various approaches were utilized in this research; however, according to the results of the experiment, the random forest algorithm exceeds all other algorithms with an accuracy of 98.5% when compared to all other algorithms.
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Swain, D., Chillur, N., Kava, M., Bhilare, A. (2023). Intelligent System for Detecting Spam Emails Using Machine Learning Classifiers. In: Chakraborty, B., Biswas, A., Chakrabarti, A. (eds) Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_5
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DOI: https://doi.org/10.1007/978-981-99-3656-4_5
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