Phishing Attacks Detection by Using Support Vector Machine
DOI:
https://doi.org/10.29304/jqcm.2023.15.2.1242Keywords:
Phishing, Attacks Detection, Kmeans, Support Vector MachineAbstract
Today's world is heading towards complete digital transformation, and with all its advantages, this transformation involves many risks, the most important of which is phishing. This article proposes a system that extracts features from all parts of the email, initially brought from different data sets, and uses one of the machine learning algorithms (K-means algorithm) to extract the valuable features, as used four methods to calculate the distance in the K-means algorithm. This work used SVM as a classifier to classify emails into phishing and legitimate and tuned its parameters to obtain a high percentage of accuracy. The proposed model gave accuracy equal to 98.8 %.
Downloads
Download data is not yet available.
References
[1] M. Almseidin, A. A. Zuraiq, M. Alkasassbeh, and N. Alnidami, “Phishing Detection Based on Machine Learning and Feature Selection Methods,” International Journal of Interactive Mobile Technologies, vol. 13, no. 12, p. 171, Dec. 2019, doi: 10.3991/ijim.v13i12.11411.
[2] A. Livara and R. M. Hernandez, An Empirical Analysis of Machine Learning Techniques in Phishing E-mail detection. 2022. doi: 10.1109/iconat53423.2022.9725434.
[3] Kathiravan, Rajasekar, Parvez, Durga, Meenakshi, and Gowsalya, “Detecting Phishing Websites using Machine Learning Algorithm,” 7th International Conference on Computing Methodologies and Communication (ICCMC), pp. 5–270, 2023.
[4] P. Bountakas and C. Xenakis, “HELPHED: Hybrid Ensemble Learning PHishing Email Detection,” Journal of Network and Computer Applications, vol. 210, p. 103545, Jan. 2023, doi: 10.1016/j.jnca.2022.103545
[5] A. Jain and B. B. Gupta, “Phishing Detection: Analysis of Visual Similarity Based Approaches,” Security and Communication Networks, vol. 2017, pp. 1–20, Jan. 2017, doi: 10.1155/2017/5421046.
[6] M. Somesha and AR. Pais, “Classification of Phishing Email Using Word Embedding and Machine Learning Techniques,” J Cyber Secur Mobil, pp. 279–320, 2022.
[7] D. N. Quang, A. Selamat, O. Krejcar, E. Herrera-Viedma, and H. Fujita, “Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions,” IEEE Access, vol. 10, pp. 36429–36463, Jan. 2022, doi: 10.1109/access.2022.3151903.
[8] P. Kalaharsha and BM. Mehtre, “Detecting Phishing Sites--An Overview,” arXiv Prepr arXiv210312739, 2021.
[9] M. Hara, A. Yamada, and Y. Miyake, Visual similarity-based phishing detection without victim site information. 2009. doi: 10.1109/cicybs.2009.4925087.
[10] S. Abdelnabi, K. Krombholz, and M. Fritz, VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity. 2020. doi: 10.1145/3372297.3417233..
[11] S. A. Salloum, T. Gaber, S. Vadera, and K. Shaalan, “A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques,” IEEE Access, vol. 10, pp. 65703–65727, Jan. 2022, doi: 10.1109/access.2022.3183083.
[12] D. M. Divakaran and A. Oest, “Phishing Detection Leveraging Machine Learning and Deep Learning: A Review,” arXiv Prepr arXiv220507411, vol. 20, no. 5, pp. 86–95, Sep. 2022, doi: 10.1109/msec.2022.3175225.
[13] A. A. Nafea, N. Omar, and M. Q. Al-Ani, “Adverse Drug Reaction Detection Using Latent Semantic Analysis,” Journal of Computer Science, vol. 17, no. 10, pp. 960–970, Oct. 2021, doi: 10.3844/jcssp.2021.960.970.
[14] M. Q. Al-Ani, N. Omar, and A. A. Nafea, “A Hybrid Method of Long Short-Term Memory and Auto-Encoder Architectures for Sarcasm Detection,” Journal of Computer Science, vol. 17, no. 11, pp. 1093–1098, Nov. 2021, doi: 10.3844/jcssp.2021.1093.1098.
[15] R. Eckhardt and S. Bagui, “A User-centric Focus for Detecting Phishing Emails,” AI, Mach Learn Deep Learn a Secur Perspect, 2023.
[16] N. Weina, X. Zhang, G. Yang, Z. Ma, and Z. Zhuo, Phishing Emails Detection Using CS-SVM. 2017. doi: 10.1109/ispa/iucc.2017.00160
[17] A. Kumar, J. M. Chatterjee, and V. García-Díaz, “A novel hybrid approach of SVM combined with NLP and probabilistic neural network for email phishing,” International Journal of Electrical and Computer Engineering, vol. 10, no. 1, p. 486, Feb. 2020, doi: 10.11591/ijece.v10i1.pp486-493.
[18] J. Rastenis, S. Ramanauskaitė, I. Suzdalev, K. Tunaitytė, J. Janulevičius, and A. Čenys, “Multi-Language Spam/Phishing Classification by Email Body Text: Toward Automated Security Incident Investigation,” Electronics, vol. 10, no. 6, p. 668, Mar. 2021, doi: 10.3390/electronics10060668.
[19] A. Mughaid, S. AlZu’bi, A. A. Hnaif, S. Taamneh, A. Alnajjar, and E. A. Elsoud, “An intelligent cyber security phishing detection system using deep learning techniques,” Cluster Computing, vol. 25, no. 6, pp. 3819–3828, May 2022, doi: 10.1007/s10586-022-03604-4.
[20] U. Butt, R. Amin, H. Aldabbas, S. Mohan, B. Alouffi, and A. Ahmadian, “Cloud-based email phishing attack using machine and deep learning algorithm,” Complex & Intelligent Systems, vol. 9, no. 3, pp. 3043–3070, Jun. 2022, doi: 10.1007/s40747-022-00760-3.
[2] A. Livara and R. M. Hernandez, An Empirical Analysis of Machine Learning Techniques in Phishing E-mail detection. 2022. doi: 10.1109/iconat53423.2022.9725434.
[3] Kathiravan, Rajasekar, Parvez, Durga, Meenakshi, and Gowsalya, “Detecting Phishing Websites using Machine Learning Algorithm,” 7th International Conference on Computing Methodologies and Communication (ICCMC), pp. 5–270, 2023.
[4] P. Bountakas and C. Xenakis, “HELPHED: Hybrid Ensemble Learning PHishing Email Detection,” Journal of Network and Computer Applications, vol. 210, p. 103545, Jan. 2023, doi: 10.1016/j.jnca.2022.103545
[5] A. Jain and B. B. Gupta, “Phishing Detection: Analysis of Visual Similarity Based Approaches,” Security and Communication Networks, vol. 2017, pp. 1–20, Jan. 2017, doi: 10.1155/2017/5421046.
[6] M. Somesha and AR. Pais, “Classification of Phishing Email Using Word Embedding and Machine Learning Techniques,” J Cyber Secur Mobil, pp. 279–320, 2022.
[7] D. N. Quang, A. Selamat, O. Krejcar, E. Herrera-Viedma, and H. Fujita, “Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions,” IEEE Access, vol. 10, pp. 36429–36463, Jan. 2022, doi: 10.1109/access.2022.3151903.
[8] P. Kalaharsha and BM. Mehtre, “Detecting Phishing Sites--An Overview,” arXiv Prepr arXiv210312739, 2021.
[9] M. Hara, A. Yamada, and Y. Miyake, Visual similarity-based phishing detection without victim site information. 2009. doi: 10.1109/cicybs.2009.4925087.
[10] S. Abdelnabi, K. Krombholz, and M. Fritz, VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity. 2020. doi: 10.1145/3372297.3417233..
[11] S. A. Salloum, T. Gaber, S. Vadera, and K. Shaalan, “A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques,” IEEE Access, vol. 10, pp. 65703–65727, Jan. 2022, doi: 10.1109/access.2022.3183083.
[12] D. M. Divakaran and A. Oest, “Phishing Detection Leveraging Machine Learning and Deep Learning: A Review,” arXiv Prepr arXiv220507411, vol. 20, no. 5, pp. 86–95, Sep. 2022, doi: 10.1109/msec.2022.3175225.
[13] A. A. Nafea, N. Omar, and M. Q. Al-Ani, “Adverse Drug Reaction Detection Using Latent Semantic Analysis,” Journal of Computer Science, vol. 17, no. 10, pp. 960–970, Oct. 2021, doi: 10.3844/jcssp.2021.960.970.
[14] M. Q. Al-Ani, N. Omar, and A. A. Nafea, “A Hybrid Method of Long Short-Term Memory and Auto-Encoder Architectures for Sarcasm Detection,” Journal of Computer Science, vol. 17, no. 11, pp. 1093–1098, Nov. 2021, doi: 10.3844/jcssp.2021.1093.1098.
[15] R. Eckhardt and S. Bagui, “A User-centric Focus for Detecting Phishing Emails,” AI, Mach Learn Deep Learn a Secur Perspect, 2023.
[16] N. Weina, X. Zhang, G. Yang, Z. Ma, and Z. Zhuo, Phishing Emails Detection Using CS-SVM. 2017. doi: 10.1109/ispa/iucc.2017.00160
[17] A. Kumar, J. M. Chatterjee, and V. García-Díaz, “A novel hybrid approach of SVM combined with NLP and probabilistic neural network for email phishing,” International Journal of Electrical and Computer Engineering, vol. 10, no. 1, p. 486, Feb. 2020, doi: 10.11591/ijece.v10i1.pp486-493.
[18] J. Rastenis, S. Ramanauskaitė, I. Suzdalev, K. Tunaitytė, J. Janulevičius, and A. Čenys, “Multi-Language Spam/Phishing Classification by Email Body Text: Toward Automated Security Incident Investigation,” Electronics, vol. 10, no. 6, p. 668, Mar. 2021, doi: 10.3390/electronics10060668.
[19] A. Mughaid, S. AlZu’bi, A. A. Hnaif, S. Taamneh, A. Alnajjar, and E. A. Elsoud, “An intelligent cyber security phishing detection system using deep learning techniques,” Cluster Computing, vol. 25, no. 6, pp. 3819–3828, May 2022, doi: 10.1007/s10586-022-03604-4.
[20] U. Butt, R. Amin, H. Aldabbas, S. Mohan, B. Alouffi, and A. Ahmadian, “Cloud-based email phishing attack using machine and deep learning algorithm,” Complex & Intelligent Systems, vol. 9, no. 3, pp. 3043–3070, Jun. 2022, doi: 10.1007/s40747-022-00760-3.
Downloads
Published
2023-09-24
How to Cite
Nabet, M. J., & George, L. E. (2023). Phishing Attacks Detection by Using Support Vector Machine. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(2), Comp Page . 180–189. https://doi.org/10.29304/jqcm.2023.15.2.1242
Issue
Section
Computer Articles
