Efficient Spam Email Detection with WEKA: A Comparative Analysis of Naïve Bayes, Support Vector Machine, and J48 Decision Tree Algorithms

Session

Computer Science and Communication Engineering

Description

As we know, email is an effective communication tool and the fastest way to send information from one place to another, saving time and cost. However, the use of email is affected by attacks that involve unwanted mail. Spam is unwanted email or can be said to represent bulk data flooding the internet with numerous duplicates of similar messages, in an attempt to force people to open them. To address the increasing problem of unwanted email on the internet (spam), interest in spam filtering also grows in line with the circumstances. In this study, I examine various spam detection techniques. In this study, I have used WEKA. In Weka, we have employed different classification algorithms such as Naïve Bayes (NB), Support Vector Machine (SMO), and J48 Decision Tree. Finally, the best classifier for unwanted email identification is determined based on algorithm accuracy and performance time.

Keywords:

Spam, Messages, WEKA, algorithms, Naïve Bayes (NB), Support Vector Machine (SMO), J48 Decision Tree, Classifier, accuracy.

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-550-95-6

Location

UBT Lipjan, Kosovo

Start Date

28-10-2023 8:00 AM

End Date

29-10-2023 6:00 PM

DOI

10.33107/ubt-ic.2023.285

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Oct 28th, 8:00 AM Oct 29th, 6:00 PM

Efficient Spam Email Detection with WEKA: A Comparative Analysis of Naïve Bayes, Support Vector Machine, and J48 Decision Tree Algorithms

UBT Lipjan, Kosovo

As we know, email is an effective communication tool and the fastest way to send information from one place to another, saving time and cost. However, the use of email is affected by attacks that involve unwanted mail. Spam is unwanted email or can be said to represent bulk data flooding the internet with numerous duplicates of similar messages, in an attempt to force people to open them. To address the increasing problem of unwanted email on the internet (spam), interest in spam filtering also grows in line with the circumstances. In this study, I examine various spam detection techniques. In this study, I have used WEKA. In Weka, we have employed different classification algorithms such as Naïve Bayes (NB), Support Vector Machine (SMO), and J48 Decision Tree. Finally, the best classifier for unwanted email identification is determined based on algorithm accuracy and performance time.