TY - GEN
T1 - Email Spam Classification
T2 - 23rd International Conference on Hybrid Intelligent Systems, HIS 2023
AU - Rahman, Atta
AU - Saraireh, Linah
AU - Youldash, Mustafa
AU - Hantom, Wafa
AU - Alkhualifi, Dania
AU - Nabil, Majed
AU - Saadeldeen, Ashraf
AU - Mahmud, Maqsood
AU - Salam, Asiya Abdus
AU - Ahmed, Mohammed Salih
AU - Gollapalli, Mohammed
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Email spamming is one of the most common scams that are occurring nowadays. In this method, an email, seemingly sent from a trusted address or sender, can be used for various purposes, including advertisement, commercial and non-commercial intents. Furthermore, it may contain content harmful to the recipient, a tactic known as phishing. Despite several email spam blocking mechanisms employed by the email service providers, it is still a major issue to handle. In this paper, several machine learning algorithms have been investigated over a realistic dataset including (but not limited to) Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) classifiers. Among them Naïve Bayes classifier exhibited the highest accuracy at 99.8% followed by AdaBoost at 96.7%. Analyses showed scheme was promising in terms of accuracy and it outperformed various state-of-the-art approaches in the literature upon comparison.
AB - Email spamming is one of the most common scams that are occurring nowadays. In this method, an email, seemingly sent from a trusted address or sender, can be used for various purposes, including advertisement, commercial and non-commercial intents. Furthermore, it may contain content harmful to the recipient, a tactic known as phishing. Despite several email spam blocking mechanisms employed by the email service providers, it is still a major issue to handle. In this paper, several machine learning algorithms have been investigated over a realistic dataset including (but not limited to) Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) classifiers. Among them Naïve Bayes classifier exhibited the highest accuracy at 99.8% followed by AdaBoost at 96.7%. Analyses showed scheme was promising in terms of accuracy and it outperformed various state-of-the-art approaches in the literature upon comparison.
KW - Decision tree (DT)
KW - Email Spam Classification
KW - Machine Learning
KW - Naïve bayes (NB)
KW - Support Vector machine (SVM)
UR - https://www.scopus.com/pages/publications/105012921758
U2 - 10.1007/978-3-031-78925-0_14
DO - 10.1007/978-3-031-78925-0_14
M3 - Conference contribution
AN - SCOPUS:105012921758
SN - 9783031789243
T3 - Lecture Notes in Networks and Systems
SP - 137
EP - 146
BT - Hybrid Intelligent Systems - 23rd International Conference on Hybrid Intelligent Systems, HIS 2023
A2 - Bajaj, Anu
A2 - Madureira, Ana Maria
A2 - Abraham, Ajith
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 11 December 2023 through 13 December 2023
ER -