Performance Comparison of Naive Bayes and Support Vector Machine Algorithms in Spambot Classification in Emails
##plugins.themes.bootstrap3.article.main##
Abstract
In the ever-growing digital era, email spam is a serious threat that affects user productivity and information security. This study aims to analyze the comparative effectiveness of Naive Bayes and SVM algorithms with radial basis function (RBF) kernels in classifying spambots in emails. The methodology used includes collecting email datasets, applying both algorithms for classification, and evaluating performance using accuracy, precision, recall, and f1-score metrics. The results showed that SVM RBF performed better than Gaussian Naive Bayes, with significant improvements in all evaluation metrics. These findings provide important insights for the development of more accurate and efficient spam detection systems, and highlight the importance of selecting appropriate algorithms in the face of complex data classification challenges.
##plugins.themes.bootstrap3.article.details##
How to Cite
Manurung, J., & Saragih, H. (2024). Performance Comparison of Naive Bayes and Support Vector Machine Algorithms in Spambot Classification in Emails. International Journal of Basic and Applied Science, 13(3), 137–145. https://doi.org/10.35335/ijobas.v13i3.522
References
R. L. Fritz and R. Vandermause, “Data collection via in-depth email interviewing: Lessons from the field,” Qual. Health Res., vol. 28, no. 10, pp. 1640–1649, 2018.
M. Sirivianos, X. Yang, and K. Kim, “FaceTrust: Collaborative Threat Mitigation Using Social Networks”.
C. Zhao, Y. Xin, X. Li, H. Zhu, Y. Yang, and Y. Chen, “An attention-based graph neural network for spam bot detection in social networks,” Appl. Sci., vol. 10, no. 22, p. 8160, 2020.
M. Al Duhayyim, H. M. Alshahrani, F. N. Al-Wesabi, M. Alamgeer, A. M. Hilal, and M. Rizwanullah, “Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks,” C. Mater. Contin., vol. 70, no. 3, pp. 6257–6270, 2022.
A. Karim, S. Azam, B. Shanmugam, K. Kannoorpatti, and M. Alazab, “A comprehensive survey for intelligent spam email detection,” Ieee Access, vol. 7, pp. 168261–168295, 2019.
Y. Perwej, S. Q. Abbas, J. P. Dixit, N. Akhtar, and A. K. Jaiswal, “A systematic literature review on the cyber security,” Int. J. Sci. Res. Manag., vol. 9, no. 12, pp. 669–710, 2021.
Z. Alkhalil, C. Hewage, L. Nawaf, and I. Khan, “Phishing attacks: A recent comprehensive study and a new anatomy,” Front. Comput. Sci., vol. 3, p. 563060, 2021.
R. M. A. Mohammad, “A lifelong spam emails classification model,” Appl. Comput. Informatics, vol. 20, no. 1/2, pp. 35–54, 2024.
A. A. Akinyelu, “Advances in spam detection for email spam, web spam, social network spam, and review spam: ML-based and nature-inspired-based techniques,” J. Comput. Secur., vol. 29, no. 5, pp. 473–529, 2021.
R. I. F. Ibadurrohman, D. R. Wijaya, and E. Hernawati, “Pengembangan Aplikasi Machine Learning Menggunakan Algoritma Support Vector Regression Dan Statistical-based Feature Selection Untuk Memprediksi Kemiskinan,” eProceedings Appl. Sci., vol. 6, no. 2, 2020.
M. A. Ramadhan and R. Andarsyah, Klasifikasi Text Spam Menggunakan Metode Support Vector Machine dan Naive Bayes. Penerbit Buku Pedia, 2022.
M. Alauthman, “Botnet spam e-mail detection using deep recurrent neural network,” Int. J, vol. 8, no. 5, pp. 1979–1986, 2020.
P. Świtalski and M. Kopówka, “Machine learning methods in e-mail spam classification,” Stud. Inform. Syst. Inf. Technol., vol. 1, 2019.
G. C. Reddy, R. R. Kumar, P. S. Kasi, N. S. Chandra, R. P. Kumar, and P. Prabakaran, “An Evaluation on the efficiency of E-Mail Spam Detection Using Naive Bayes Classifier,” Int. J. Innov. Res. Eng. Manag., vol. 9, no. 2, pp. 648–652, 2022.
Á. F. Gambín, A. Yazidi, A. Vasilakos, H. Haugerud, and Y. Djenouri, “Deepfakes: current and future trends,” Artif. Intell. Rev., vol. 57, no. 3, p. 64, 2024.
T. Gangavarapu, C. D. Jaidhar, and B. Chanduka, “Applicability of machine learning in spam and phishing email filtering: review and approaches,” Artif. Intell. Rev., vol. 53, no. 7, pp. 5019–5081, 2020.
S. Rathore, V. Loia, and J. H. Park, “SpamSpotter: an efficient spammer detection framework based on intelligent decision support system on facebook,” Appl. Soft Comput., vol. 67, pp. 920–932, 2018.
A.-Z. Ala’M, A. M. Mora, and H. Faris, “A multilingual spam reviews detection based on pre-trained word embedding and weighted swarm support vector machines,” IEEE Access, 2023.
A. Alzahrani and D. B. Rawat, “Comparative study of machine learning algorithms for SMS spam detection,” in 2019 SoutheastCon, 2019, pp. 1–6.
T. M. Ma, K. Yamamori, and A. Thida, “A comparative approach to Naïve Bayes classifier and support vector machine for email spam classification,” in 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), 2020, pp. 324–326.
T. Wu, S. Wen, Y. Xiang, and W. Zhou, “Twitter spam detection: Survey of new approaches and comparative study,” Comput. Secur., vol. 76, pp. 265–284, 2018.
N. Ahmed, R. Amin, H. Aldabbas, D. Koundal, B. Alouffi, and T. Shah, “Machine learning techniques for spam detection in email and IoT platforms: analysis and research challenges,” Secur. Commun. Networks, vol. 2022, no. 1, p. 1862888, 2022.
Z. Ellaky, F. Benabbou, and S. Ouahabi, “Systematic literature review of social media bots detection systems,” J. King Saud Univ. Inf. Sci., vol. 35, no. 5, p. 101551, 2023.
A. B. A. Girgin and G. Gümüşçekiçci, “From past to present: Spam detection and identifying opinion leaders in social networks,” Sigma J. Eng. Nat. Sci., vol. 40, no. 2, pp. 441–463, 2022.
M. D. D. Chathurangi, M. G. K. Nayanathara, K. Gunapala, G. Dayananda, K. Y. Abeywardena, and D. Siriwardana, “Detecting Cyberbullying, Spam & Bot Behavior and Fake News in Social Media Accounts Using Machine Learning,” open Sci. index 18 2024, vol. 20, p. 17, 2024.
P. Andriotis and A. Takasu, “Emotional bots: content-based spammer detection on social media,” in 2018 IEEE international workshop on information forensics and security (WIFS), 2018, pp. 1–8.
M. Sirivianos, X. Yang, and K. Kim, “FaceTrust: Collaborative Threat Mitigation Using Social Networks”.
C. Zhao, Y. Xin, X. Li, H. Zhu, Y. Yang, and Y. Chen, “An attention-based graph neural network for spam bot detection in social networks,” Appl. Sci., vol. 10, no. 22, p. 8160, 2020.
M. Al Duhayyim, H. M. Alshahrani, F. N. Al-Wesabi, M. Alamgeer, A. M. Hilal, and M. Rizwanullah, “Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks,” C. Mater. Contin., vol. 70, no. 3, pp. 6257–6270, 2022.
A. Karim, S. Azam, B. Shanmugam, K. Kannoorpatti, and M. Alazab, “A comprehensive survey for intelligent spam email detection,” Ieee Access, vol. 7, pp. 168261–168295, 2019.
Y. Perwej, S. Q. Abbas, J. P. Dixit, N. Akhtar, and A. K. Jaiswal, “A systematic literature review on the cyber security,” Int. J. Sci. Res. Manag., vol. 9, no. 12, pp. 669–710, 2021.
Z. Alkhalil, C. Hewage, L. Nawaf, and I. Khan, “Phishing attacks: A recent comprehensive study and a new anatomy,” Front. Comput. Sci., vol. 3, p. 563060, 2021.
R. M. A. Mohammad, “A lifelong spam emails classification model,” Appl. Comput. Informatics, vol. 20, no. 1/2, pp. 35–54, 2024.
A. A. Akinyelu, “Advances in spam detection for email spam, web spam, social network spam, and review spam: ML-based and nature-inspired-based techniques,” J. Comput. Secur., vol. 29, no. 5, pp. 473–529, 2021.
R. I. F. Ibadurrohman, D. R. Wijaya, and E. Hernawati, “Pengembangan Aplikasi Machine Learning Menggunakan Algoritma Support Vector Regression Dan Statistical-based Feature Selection Untuk Memprediksi Kemiskinan,” eProceedings Appl. Sci., vol. 6, no. 2, 2020.
M. A. Ramadhan and R. Andarsyah, Klasifikasi Text Spam Menggunakan Metode Support Vector Machine dan Naive Bayes. Penerbit Buku Pedia, 2022.
M. Alauthman, “Botnet spam e-mail detection using deep recurrent neural network,” Int. J, vol. 8, no. 5, pp. 1979–1986, 2020.
P. Świtalski and M. Kopówka, “Machine learning methods in e-mail spam classification,” Stud. Inform. Syst. Inf. Technol., vol. 1, 2019.
G. C. Reddy, R. R. Kumar, P. S. Kasi, N. S. Chandra, R. P. Kumar, and P. Prabakaran, “An Evaluation on the efficiency of E-Mail Spam Detection Using Naive Bayes Classifier,” Int. J. Innov. Res. Eng. Manag., vol. 9, no. 2, pp. 648–652, 2022.
Á. F. Gambín, A. Yazidi, A. Vasilakos, H. Haugerud, and Y. Djenouri, “Deepfakes: current and future trends,” Artif. Intell. Rev., vol. 57, no. 3, p. 64, 2024.
T. Gangavarapu, C. D. Jaidhar, and B. Chanduka, “Applicability of machine learning in spam and phishing email filtering: review and approaches,” Artif. Intell. Rev., vol. 53, no. 7, pp. 5019–5081, 2020.
S. Rathore, V. Loia, and J. H. Park, “SpamSpotter: an efficient spammer detection framework based on intelligent decision support system on facebook,” Appl. Soft Comput., vol. 67, pp. 920–932, 2018.
A.-Z. Ala’M, A. M. Mora, and H. Faris, “A multilingual spam reviews detection based on pre-trained word embedding and weighted swarm support vector machines,” IEEE Access, 2023.
A. Alzahrani and D. B. Rawat, “Comparative study of machine learning algorithms for SMS spam detection,” in 2019 SoutheastCon, 2019, pp. 1–6.
T. M. Ma, K. Yamamori, and A. Thida, “A comparative approach to Naïve Bayes classifier and support vector machine for email spam classification,” in 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), 2020, pp. 324–326.
T. Wu, S. Wen, Y. Xiang, and W. Zhou, “Twitter spam detection: Survey of new approaches and comparative study,” Comput. Secur., vol. 76, pp. 265–284, 2018.
N. Ahmed, R. Amin, H. Aldabbas, D. Koundal, B. Alouffi, and T. Shah, “Machine learning techniques for spam detection in email and IoT platforms: analysis and research challenges,” Secur. Commun. Networks, vol. 2022, no. 1, p. 1862888, 2022.
Z. Ellaky, F. Benabbou, and S. Ouahabi, “Systematic literature review of social media bots detection systems,” J. King Saud Univ. Inf. Sci., vol. 35, no. 5, p. 101551, 2023.
A. B. A. Girgin and G. Gümüşçekiçci, “From past to present: Spam detection and identifying opinion leaders in social networks,” Sigma J. Eng. Nat. Sci., vol. 40, no. 2, pp. 441–463, 2022.
M. D. D. Chathurangi, M. G. K. Nayanathara, K. Gunapala, G. Dayananda, K. Y. Abeywardena, and D. Siriwardana, “Detecting Cyberbullying, Spam & Bot Behavior and Fake News in Social Media Accounts Using Machine Learning,” open Sci. index 18 2024, vol. 20, p. 17, 2024.
P. Andriotis and A. Takasu, “Emotional bots: content-based spammer detection on social media,” in 2018 IEEE international workshop on information forensics and security (WIFS), 2018, pp. 1–8.
Copyright and Licensing
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.