Enhancеd Analysis Approach to Detect Phishing Attacks During COVID-19 Crisis

1. Phishing|General Phishing Information and Prevention Tips (Accessed 18 February 2022). https://www.phishing.org/ Search in Google Scholar

2. Internet-Statistics (Online). https://www.broadbandsearch.net/blog/internet-statistics Search in Google Scholar

3. Whitman, M. E., H. J. Mattord. Principles of Information Security. Cengage Learning, 2011. Search in Google Scholar

4. Trautman, L. J., M. Hussein, E. U. Opara, M. J. Molesky, S. Rahman. Posted: No Phishing. – In: Emory Corp. Gov. Account. Rev., 2020. Search in Google Scholar

5. Alqurashi, R. K., M. A. AlZain, B. Soh, M. Masud, J. Al-Amri. Cyber Attacks and Impacts: A Case Study in Saudi Arabia. – Int. J., Vol. 9, 2020, No 1.10.30534/ijatcse/2020/33912020 Search in Google Scholar

6. Sahingoz, O. K., E. Buber, O. Demir, B. Diri. Machine Learning Based Phishing Detection from URLs. – Expert Syst. Appl., Vol. 117, 2019, pp. 345-357.10.1016/j.eswa.2018.09.029 Search in Google Scholar

7. Buczak, A. L., E. Guven. A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. – IEEE Commun. Surv. Tutorials, Vol. 18, 2015, No 2, pp. 1153-1176.10.1109/COMST.2015.2494502 Search in Google Scholar

8. Luo, Z. A Study of Accuracy and Reliability of CBIR-Based Phishing Filter. Purdue University, 2013. Search in Google Scholar

9. Zouina, M., B. Outtaj. A Novel Lightweight URL Phishing Detection System Using SVM and Similarity Index. – Human-Centric Comput. Inf. Sci., Vol. 7, 2017, No 1, pp. 1-13.10.1186/s13673-017-0098-1 Search in Google Scholar

10. Anand, A., K. Gorde, J. R. A. Moniz, N. Park, T. Chakraborty, B.-T. Chu. Phishing URL Detection with Oversampling Based on Text Generative Adversarial Networks. – In: Proc. of 2018 IEEE International Conference on Big Data (Big Data’18), 2018, pp. 1168-1177.10.1109/BigData.2018.8622547 Search in Google Scholar

11. Shirazi, H., S. R. Muramudalige, I. Ray, A. P. Jayasumana. Improved Phishing Detection Algorithms Using Adversarial Autoencoder Synthesized Data. – In: Proc. of 2020 IEEE 45th Conference on Local Computer Networks (LCN’20), 2020, pp. 24-32.10.1109/LCN48667.2020.9314775 Search in Google Scholar

12. Xiao, X., D. Zhang, G. Hu, Y. Jiang, S. Xia. CNN-MHSA: A Convolutional Neural Network and Multi-Head Self-Attention Combined Approach for Detecting Phishing Websites. – Neural Networks, Vol. 125, 2020, pp. 303-312.10.1016/j.neunet.2020.02.01332172140 Search in Google Scholar

13. Kamran, S. A., S. Sengupta, A. Tavakkoli. Semi-Supervised Conditional GAN for Simultaneous Generation and Detection of Phishing URLs: A Game Theoretic Perspective. arXiv Prepr. arXiv2108.01852, 2021. Search in Google Scholar

14. Yerima, S. Y., M. K. Alzaylaee. High Accuracy Phishing Detection Based on Convolutional Neural Networks. – In: Proc. of 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS’20), 2020, pp. 1-6.10.1109/ICCAIS48893.2020.9096869 Search in Google Scholar

15. Yi, P., Y. Guan, F. Zou, Y. Yao, W. Wang, T. Zhu. Web Phishing Detection Using a Deep Learning Framework. – Wirel. Commun. Mob. Comput., Vol. 2018, 2018.10.1155/2018/4678746 Search in Google Scholar

16. Abutair, H., A. Belghith, S. AlAhmadi. CBR-PDS: A Case-Based Reasoning Phishing Detection System. – J. Ambient Intell. Humaniz. Comput., Vol. 10, 2019, No 7, pp. 2593-2606.10.1007/s12652-018-0736-0 Search in Google Scholar

17. Adebowale, M. A., K. T. Lwin, E. Sanchez, M. A. Hossain. Intelligent Web-Phishing Detection and Protection Scheme Using Integrated Features of Images, Frames and Text. – Expert Syst. Appl., Vol. 115, 2019, pp. 300-313.10.1016/j.eswa.2018.07.067 Search in Google Scholar

18. Babagoli, M., M. P. Aghababa, V. Solouk. Heuristic Nonlinear Regression Strategy for Detecting Phishing Websites. – Soft Comput., Vol. 23, 2019, No 12, pp. 4315-4327.10.1007/s00500-018-3084-2 Search in Google Scholar

19. Ferreira, R. P., et al. Artificial Neural Network for Websites Classification with Phishing Characteristics. – Soc. Netw., Vol. 7, 2018, No 2, p. 97.10.4236/sn.2018.72008 Search in Google Scholar

20. Korkmaz, M., O. K. Sahingoz, B. Diri. Detection of Phishing Websites by Using Machine Learning-Based URL Analysis. – In: Proc. of 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT’20), 2020, pp. 1-7.10.1109/ICCCNT49239.2020.9225561 Search in Google Scholar

21. Alam, M. N., D. Sarma, F. F. Lima, I. Saha, S. Hossain. Phishing Attacks Detection Using Machine Learning Approach. – In: Proc. of 3rd International Conference on Smart Systems and Inventive Technology (ICSSIT’20), 2020, pp. 1173-1179.10.1109/ICSSIT48917.2020.9214225 Search in Google Scholar

22. Kumar, J., A. Santhanavijayan, B. Janet, B. Rajendran, B. S. Bindhumad-hava. Phishing Website Classification and Detection Using Machine Learning. – In: Proc. of 2020 International Conference on Computer Communication and Informatics (ICCCI’20), 2020, pp. 1-6.10.1109/ICCCI48352.2020.9104161 Search in Google Scholar

23. Do, N. Q., A. Selamat, O. Krejcar, T. Yokoi, H. Fujita. Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study. – Appl. Sci., Vol. 11, 2021, No 19, p. 9210.10.3390/app11199210 Search in Google Scholar

24. Yang, R., K. Zheng, B. Wu, C. Wu, X. Wang. Phishing Website Detection Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning. – Sensors, Vol. 21, 2021, No 24, p. 8281.10.3390/s21248281870938034960375 Search in Google Scholar

25. Dangwal, S., A.-N. Moldovan. Feature Selection for Machine Learning-Based Phishing Websites Detection. – In: Proc. of 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA’21), 2021, pp. 1-6.10.1109/CyberSA52016.2021.9478242 Search in Google Scholar

26. Berners-Lee, T., L. Masinter, M. McCahill. Uniform Resource Locators (URL). 1994.10.17487/rfc1738 Search in Google Scholar

27. Bibi, I., A. Akhunzada, J. Malik, J. Iqbal, A. Mussaddiq, S. Kim. A Dynamic DL-Driven Architecture to Combat Sophisticated Android Malware. – IEEE Access, Vol. 8, 2020, pp. 129600-129612.10.1109/ACCESS.2020.3009819 Search in Google Scholar

28. Kula, S., M. Choraś, R. Kozik, P. Ksieniewicz, M. Woźniak. Sentiment Analysis for Fake News Detection by Means of Neural Networks. – In: Proc. of International Conference on Computational Science, 2020, pp. 653-666.10.1007/978-3-030-50423-6_49 Search in Google Scholar

29. Chung, J., C. Gulcehre, K. Cho, Y. Bengio. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv Prepr. arXiv1412.3555, 2014. Search in Google Scholar

30. Kalman, B. L., S. C. Kwasny. Why Tanh: Choosing a Sigmoidal Function. – In: Proc. of 1992 IJCNN International Joint Conference on Neural Networks, Vol. 4, 1992, pp. 578-581. Search in Google Scholar

31. Baldi, P., S. Brunak, Y. Chauvin, C. A. F. Andersen, H. Nielsen. Assessing the Accuracy of Prediction Algorithms for Classification: An Overview. – Bioinformatics, Vol. 16, 2000, No 5, pp. 412-424.10.1093/bioinformatics/16.5.41210871264 Search in Google Scholar

32. Saito, T., M. Rehmsmeier. The Precision-Recall Plot is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. – PLoS One, Vol. 10, 2015, No 3, p. e0118432.10.1371/journal.pone.0118432434980025738806 Search in Google Scholar

33. Chatterjee, M., A.-S. Namin. Detecting Phishing Websites through Deep Reinforcement Learning. – In: Proc. of 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC’19), Vol. 2, 2019, pp. 227-232.10.1109/COMPSAC.2019.10211 Search in Google Scholar

34. Zhang, W., Q. Jiang, L. Chen, C. Li. Two-Stage ELM for Phishing Web Pages Detection Using Hybrid Features. – World Wide Web, Vol. 20, 2017, No 4, pp. 797-813.10.1007/s11280-016-0418-9 Search in Google Scholar

35. El-Alfy, E.-S. M. Detection of Phishing Websites Based on Probabilistic Neural Networks and K-Medoids Clustering. – Comput. J., Vol. 60, 2017, No 12, pp. 1745-1759.10.1093/comjnl/bxx035 Search in Google Scholar

36. Montazer, G. A., S. ArabYarmohammadi. Detection of Phishing Attacks in Iranian e-Banking Using a Fuzzy-Rough Hybrid System. – Appl. Soft Comput., Vol. 35, 2015, pp. 482-492.10.1016/j.asoc.2015.05.059 Search in Google Scholar

37. Yadollahi, M. M., F. Shoeleh, E. Serkani, A. Madani, H. Gharaee. An Adaptive Machine Learning Based Approach for Phishing Detection Using Hybrid Features. – In: Proc. of 2019 5th International Conference on Web Research (ICWR’19), 2019, pp. 281-286.10.1109/ICWR.2019.8765265 Search in Google Scholar

38. Alshira’h, M., M. Al-Fawa’reh. Detecting Phishing Urls Using machine Learning Lexical Feature-Based Analysis. – Int. J. Adv. Trends Comput. Sci. Eng., Vol. 9, 2020, No 4, pp. 5828-5837.10.30534/ijatcse/2020/242942020 Search in Google Scholar

39. Al-Ruzouq, R. et al. Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review. – Remote Sens., Vol. 12, 2020, No 20, p. 3338.10.3390/rs12203338 Search in Google Scholar

40. Deep Learning – What is the Complexity of a Bidirectional Recurrent Neural Network? – Data Science Stack Exchange (Accessed 18 February 2022). https://datascience.stackexchange.com/questions/82766/what-is-the-complexity-of-a-bidirectional-recurrent-neural-network Search in Google Scholar

41. Computational Complexity of ML Models|by Paritosh Kumar|Analytics Vidhya|Medium (Accessed 18 February 2022). https://medium.com/analytics-vidhya/time-complexity-of-ml-models-4ec39fad2770 Search in Google Scholar