Augmented Framework for Enhanced Phishing Detection and Prevention
DOI:
https://doi.org/10.1109/CSDGAIS64098.2024.11064828الكلمات المفتاحية:
Filtering، Phishing، Prevention and mitigation، Machine learning، Network security، Data collection، Data models، Internet، Fraud، Reliabilityالملخص
Network security has become a hot topic as the Internet has grown in popularity. Phishing attacks are a type of cybercrime when a hacker poses as a reliable source to gain confidential data from a user of the internet. Therefore, more efficient phishing detection is required for better cyber defense. A framework for improving phishing detection based on machine learning is presented in this study. The framework attempts to enhance the speed-to-accuracy ratio in identifying fraud risks by examining the body of currently available literature on phishing attacks and associated remedies. The proposed model improves the effectiveness of phishing detection systems by highlighting the importance of data collection and filtering procedures. To create new security measures and respond to evolving threats, regular updates and ongoing research are necessary. Therefore, this study provides information for upcoming studies on cybersecurity and the preservation of personal data.المراجع
Akinyede, R. O., & Adelakun, J. A. (2018). Detection and prevention of phishing attack using linkguard algorithm. Journal of Information, 4(1), 10–23.
Alam, M. N., et al. (2020). Phishing attacks detection using machine learning approach. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT).
Alazaidah, R., et al. (2024). Website phishing detection using machine learning techniques. Journal of Statistics Applications & Probability, 13(1), 119–129.
Aldakheel, E. A., Zakariah, M., Gashgari, G. A., Almarshad, F. A., & Alzahrani, A. I. A. (2023). A deep learning-based innovative technique for phishing detection in modern security with uniform resource locators. Sensors, 23(9), 4403. https://doi.org/10.3390/s23094403
Alkhalil, Z., Hewage, C., Nawaf, L., & Khan, I. (2021). Phishing attacks: A recent comprehensive study and a new anatomy. Frontiers in Computer Science, 3. https://doi.org/10.3389/fcomp.2021.563060
Alnemari, S., & Alshammari, M. (2023). Detecting phishing domains using machine learning. Applied Sciences, 13(8), 4649.
Al-Otaibi, A., & Alsuwat, E. (2020). A study on social engineering attacks: Phishing attack. International Journal of Recent Advances in Multidisciplinary Research.
Basit, A., et al. (2020). A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommunication Systems, 76(1).
Bhadane, A., & Mane, S. B. (2019). Detecting lateral spear phishing attacks in organisations. IET Information Security, 13(2), 133–140.
Harrison, B., et al. (2016). Individual processing of phishing emails. Online Information Review, 40(2), 265–281.
Javeed, D., & MohammedBadamasi, U. (2020). Man in the middle attacks: Analysis, motivation and prevention. International Journal of Computer Networks and Communications Security, 8(7), 52–58.
Khan, A. I., & Unhelkar, B. (2024). An enhanced anti-phishing technique for social media users: A multilayer Q-learning approach. International Journal of Advanced Computer Science and Applications, 15(1).
Mishra, S., & Soni, D. (2019). A content-based approach for detecting smishing in mobile environment. SSRN Electronic Journal.
Mishra, S., & Soni, D. (2021). DSmishSMS: A system to detect smishing SMS. Neural Computing and Applications, 35(7), 4975–4992.
Mosa, D. T. (2023). Machine learning techniques for detecting phishing URL attacks. Computers, Materials & Continua, 75(1), 1271–1290.
Nanda, A., & Gupta, H. (2015). Anti-phishing techniques in cryptography. International Journal of Electrical and Computer Engineering (IJECE), 5(6), 1511.
Odeh, A., & Keshta, I. (2021). PhiBoost: A novel phishing detection model using adaptive boosting approach. Jordanian Journal of Computers and Information Technology, 7(1), 64.
Orunsolu, A. A., Alaran, M. A., Adebayo, A. A., Kareem, S. O., & Oke, A. (2017). A lightweight anti-phishing technique for mobile phone. Acta Informatica Pragensia, 6(2), 114–123. https://doi.org/10.18267/j.aip.104
Rani, T. J., et al. (2021). SMS spam detection framework using machine learning algorithms and neural networks. International Journal of Computer Science and Mobile Computing, 10(6), 10–19.
Salahdine, F., & Kaabouch, N. (2019). Social engineering attacks: A survey. Future Internet, 11(4), 89.
Sarker, O., et al. (2024). A multi-vocal literature review on challenges and critical success factors of phishing education, training and awareness. Journal of Systems and Software, 208, 111899.
Sonowal, G. (2020). Detecting phishing SMS based on multiple correlation algorithms. SN Computer Science, 1(6).
Tandale, K. D., & Pawar, S. N. (2020). Different types of phishing attacks and detection techniques: A review. In 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC).
Tang, L., & Mahmoud, Q. H. (2021). A survey of machine learning-based solutions for phishing website detection. Machine Learning and Knowledge Extraction, 3(3), 672–694.
Upadhyay, H., & Panchal, S. D. (n.d.). Unique approach to detect and prevent against pharming attack.
Vadera, S., Salloum, S., Gaber, T., & Shaalan, K. (2022). A systematic literature review on phishing email detection using natural language processing techniques. IEEE Access, 10, 65703–65727.
Younis, Y. A., & Musbah, M. (2020). A framework to protect against phishing attacks. In Proceedings of the 6th International Conference on Engineering and MIS (ICEMIS).
Zhao, G., Yang, P., & Zeng, P. (2019). Phishing website detection based on multidimensional features driven by deep learning. IEEE Access, 7, 15196–15209.
