Page 132 - The-5th-MCAIT2021-eProceeding

Acknowledgements
This research was supported by Universiti Kebangsaan Malaysia under research code [GGP-2020-041
UKM].

References
Bagui, S., Nandi, D., Bagui, S., & White, R. J. (2019). Classifying Phishing Email Using Machine Learning
and Deep Learning. 2019 International Conference on Cyber Security and Protection of Digital Services
(Cyber Security). Published. https://doi.org/10.1109/cybersecpods.2019.8885143
Birlea, M.C. (2020). Phishing Attacks: Detection and Prevention. ArXiv, abs/2004.01556.
Dakpa, T., & Augustine, P. (2017). Study of Phishing Attacks and Preventions. International Journal of
Computer Applications, 163(2), 5–8. https://doi.org/10.5120/ijca2017913461
Das, S., Kim, A., Tingle, Z., & Nippert-Eng, C. (2019). All About Phishing: Exploring User Research through
a Systematic Literature Review. ArXiv, abs/1908.05897.
El Aassal, A., Baki, S., Das, A., & Verma, R. M. (2020). An In-Depth Benchmarking and Evaluation of Phishing
Detection Research for Security Needs. IEEE Access, 8, 22170–22192.
https://doi.org/10.1109/access.2020.2969780
Fang, Y., Zhang, C., Huang, C., Liu, L., & Yang, Y. (2019). Phishing Email Detection Using Improved RCNN
Model With Multilevel Vectors and Attention Mechanism. IEEE Access, 7, 56329–56340.
https://doi.org/10.1109/access.2019.2913705
Gomez Adorno, H. M., Rios, G., Posadas Durán, J. P., Sidorov, G., & Sierra, G. (2018). Stylometry-based
Approach for Detecting Writing Style Changes in Literary Texts. Computación y Sistemas, 22(1).
https://doi.org/10.13053/cys-22-1-2882
Imaduddin, H., Widyawan, & Fauziati, S. (2019). Word Embedding Comparison for Indonesian Language
Sentiment Analysis. 2019 International Conference of Artificial Intelligence and Information Technology
(ICAIIT). Published. https://doi.org/10.1109/icaiit.2019.8834536
Kumar, A., Chatterjee, J., & Díaz, V.G. (2020). A novel hybrid approach of SVM combined with NLP and
probabilistic neural network for email phishing. International Journal of Electrical and Computer Engineering,
10, 486-493.
Kumar, S., Faizan, A., Viinikainen, A., & Hamalainen, T. (2018). MLSPD - Machine Learning Based Spam
and Phishing Detection. Computational Data and Social Networks, 510–522. https://doi.org/10.1007/978-3
030-04648-4_43
Lagutina, K., Lagutina, N., Boychuk, E., & Paramonov, I. (2020). The Influence of Different Stylometric
Features on the Classification of Prose by Centuries. 2020 27th Conference of Open Innovations Association
(FRUCT). Published. https://doi.org/10.23919/fruct49677.2020.9211036
Sharon Belvisi, N. M., Muhammad, N., & Alonso-Fernandez, F. (2020). Forensic Authorship Analysis of
Microblogging Texts Using N-Grams and Stylometric Features. 2020 8th International Workshop on
Biometrics and Forensics (IWBF). Published. https://doi.org/10.1109/iwbf49977.2020.9107953
Xiujuan, W., Chenxi, Z., Kang-feng, Z., Haoyang, T., & Yuanrui, T. (2019). Detecting Spear-phishing Emails
Based on Authentication. 2019 IEEE 4th International Conference on Computer and Communication Systems
(ICCCS).

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Artificial Intelligence in the 4th Industrial Revolution