A Novel Model Based on Ensemble Learning for Phishing Attack
Abstract
With the increase in the speed of the internet environment and the development of the infrastructures used, people have started to perform most of their work online. As much as this makes life easier, it also increases the possibility of being attacked by malicious people. Attackers can activate a phishing attack that aims to steal information from victims by creating copied, fake websites. While this attack is very old and somewhat simple, it can still be effective due to low IT literacy. People can enter their information on these fake websites out of spontaneity or ignorance or good intentions and be exposed to Phishing attacks. The compromise of a user's account information also puts at risk the security of the organization or institution to which it is connected. In this study, we propose a new machine learning-based ensemble model with feature selection methods to detect phishing attacks. Also, an ablation study is presented to measure the effect of different feature selection methods. The proposed model which we named as NaiveStackingSymmetric (NSS) is analyzed using the widely used accuracy (ACC), the area under curve (AUC), and F-score metrics as well as the polygon area metric (PAM), and it is shown that it outperforms other studies in the literature using the same dataset.
Keywords
Phishing attack, ensemble learning, malicious URL, stacking, information security
References
- [1] A. Karakaya and S. Akleylek, “A survey on security threats and authentication approaches in wireless sensor networks,” in 2018 6th International Symposium on Digital Forensic and Security (ISDFS), 2018, pp. 1–4. doi: 10.1109/ISDFS.2018.8355381.
- [2] A. Karakaya and F. Arat, “A Survey on Security Requirements, Threats and Protocols in Industrial Internet of Things,” International Journal of Information Security Science, vol. 10, no. 4. Şeref SAĞIROĞLU, pp. 138–152, 2021.
- [3] K. Krombholz, H. Hobel, M. Huber, and E. Weippl, “Advanced social engineering attacks,” J. Inf. Secur. Appl., vol. 22, pp. 113–122, 2015.
- [4] A. Almomani et al., “Phishing website detection with semantic features based on machine learning classifiers: A comparative study,” Int. J. Semant. Web Inf. Syst., vol. 18, no. 1, pp. 1–24, 2022.
- [5] S. R. Sharma, B. Singh, and M. Kaur, “Improving the classification of phishing websites using a hybrid algorithm,” Comput. Intell., vol. 38, no. 2, pp. 667–689, 2022.
- [6] O. Aydemir, “A new performance evaluation metric for classifiers: polygon area metric,” J. Classif., vol. 38, pp. 16–26, 2021.
- [7] S. Maurya and A. Jain, “Malicious Website Detection Based on URL Classification: A Comparative Analysis,” in Proceedings of Third International Conference on Computing, Communications, and Cyber-Security: IC4S 2021, 2022, pp. 249–260.
- [8] H. Bouijij, A. Berqia, and H. Saliah-Hassan, “Phishing URL classification using Extra-Tree and DNN,” in 2022 10th International Symposium on Digital Forensics and Security (ISDFS), 2022, pp. 1–6.
- [9] J. V. Cubas and G. M. Niño, “Modelo de machine learning en la detección de sitios web phishing,” Rev. Ibérica Sist. e Tecnol. Informação, no. E52, pp. 161–173, 2022.
- [10] M. A. A. Siddiq, M. Arifuzzaman, and M. S. Islam, “Phishing Website Detection using Deep Learning,” in Proceedings of the 2nd International Conference on Computing Advancements, 2022, pp. 83–88.