ENHANCING CYBERSECURITY THROUGH MACHINE LEARNING FOR THREAT PREDICTION AND DETECTION

Conclusion The findings collectively of this research on detection using machine learning over phishing websites sum-marize well. As such, ML-based methods, as it had been in many of the referred works, offer a bright future in anti-phishing campaigns [1-10]. This again underscores the importance of feature engineering as well as proper selection of algorithms, reflecting an important focus in comparison studies of different algorithms ([1, 5, 6, 8, 9]) and discussed in broader reviews ([2, 3, 4, 10]). In the conclusion, the benefits of using diverse feature sets are stressed, combining URL-based features as emphasized in [7, 10] and content- and network-based features, a common practice in most studies ([1, 5, 8]). The superiority of ensemble methods such as Random Forest, typically shown in comparison studies ([1, 6]), is also mentioned. In addition, the conclusion points toward future directions by proposing the incorporation of real-time data feeds and active learning strategies for better adaptability, as has been touched upon in some review papers ([2, 3, 4, 10]). Finally, it stresses the need for continued innovation and collaboration to effectively counter the evolving nature of phishing attacks, a call implicitly supported by the ongoing research represented in the cited literature.

[1]  Dharmendra Singh Kushwaha, Alok Kumar Singh, and Vimal Kumar. (2021). ”Phishing Website Detection Using Machine Learning Tech-niques.” International Journal of Innovative Technology and Exploring Engineering (IJITEE), 10(7), 3056-3062. [2]  Javed, M. A., and Sajid, M. A. (2019). ”Detecting Phishing Websites Us-ing Machine Learning.” In Proceedings of the International Conference on Engineering and Emerging Technologies (ICEET), 1-6. [3]  Singh, V., and Gupta, N. (2019). ”A Comparative Study of Machine Learning Algorithms for Phishing Website Detection.” International Journal of Engineering and Advanced Technology (IJEAT), 9(3), 1569-1574. [4]  Akbar, M. F., Akbar, S., and Javaid, M. A. (2020). ”A Comprehensive Review on Machine Learning-Based Techniques for Phishing Website Detection.” Journal of Computer Networks and Communications, 2020, Article ID 8861328. [5]  Jagtap, A., Yadav, A., and Patil, K. (2018). ”Phishing Website Detection Using Machine Learning Algorithms.” International Journal of Computer Science and Mobile Computing (IJCSMC), 7(12), 263-268. [6]                              Ahmad, M., and Faisal, A. (2021). ”A Survey on Phishing Website Detection Techniques Using Machine Learning.” In Proceedings of the 4th International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 1-6. [7]                              Mirzal, A. A., Wijaya, D. K., and Haryono, A. (2020). ”Machine Learning for Phishing Website Detection: A Review.” In Proceedings of the International Conference on Electrical Engineering and Informatics (ICEEI), 1-6. [8]                              Al-Kasassbeh, M., Al-Ebbini, L., and Al-Azzam, O. (2018). ”Detecting Phishing Websites Using Machine Learning Algorithms.” In Proceedings of the International Conference on Big Data and Advanced Wireless Technologies (BDAW), 1-6. [9]                              Islam, M. A., and Kamruzzaman, S. M. (2017). ”Phishing Website De-tection Using Machine Learning Techniques: A Review.” In Proceedings of the 2nd International Conference on Computer and Communication Systems (ICCCS), 419-424. [10]                          Salekin, S., and Hossain, M. A. (2019). ”A Machine Learning Approach to Phishing Detection Using URL Features.” In Proceedings of the In-ternational Conference on Networking, Communication and Computing (ICNCC), 1-6.