PhishNot: A Cloud-Based Machine-Learning Approach to Phishing URL Detection

Computer Networks - Tập 218 - Trang 109407 - 2022
Mohammed M. Alani1,2, Hissam Tawfik3,4
1Computer Science Department, Toronto Metropolitan University, Toronto, Canada
2School of IT Administration and Security, Seneca College of Applied Arts and Technology, Toronto, Canada
3College of Engineering, University of Sharjah, Sharjah, United Arab Emirates
4School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, United Kingdom

Tài liệu tham khảo

Khonji, 2013, Phishing detection: a literature survey, IEEE Commun. Surv. Tutor., 15, 2091, 10.1109/SURV.2013.032213.00009 Caputo, 2013, Going spear phishing: Exploring embedded training and awareness, IEEE Secur. Priv., 12, 28, 10.1109/MSP.2013.106 2021, Time to report phishing email 2020 — statista, Statista 2022 Yadav, 2015, Technical aspects of cyber kill chain, 438 Sonowal, 2020, PhiDMA–A phishing detection model with multi-filter approach, J. King Saud Univ. Comput. Inf. Sci., 32, 99 Rao, 2019, Jail-Phish: An improved search engine based phishing detection system, Comput. Secur., 83, 246, 10.1016/j.cose.2019.02.011 Chin, 2018, Phishlimiter: A phishing detection and mitigation approach using software-defined networking, IEEE Access, 6, 42516, 10.1109/ACCESS.2018.2837889 Wei, 2019, A deep-learning-driven light-weight phishing detection sensor, Sensors, 19, 4258, 10.3390/s19194258 Sahingoz, 2019, Machine learning based phishing detection from URLs, Expert Syst. Appl., 117, 345, 10.1016/j.eswa.2018.09.029 Chiew, 2019, A new hybrid ensemble feature selection framework for machine learning-based phishing detection system, Inform. Sci., 484, 153, 10.1016/j.ins.2019.01.064 Jain, 2019, A machine learning based approach for phishing detection using hyperlinks information, J. Ambient Intell. Humaniz. Comput., 10, 2015, 10.1007/s12652-018-0798-z Abutair, 2019, CBR-PDS: a case-based reasoning phishing detection system, J. Ambient Intell. Humaniz. Comput., 10, 2593, 10.1007/s12652-018-0736-0 Zhu, 2020, DTOF-ANN: An artificial neural network phishing detection model based on decision tree and optimal features, Appl. Soft Comput., 95, 10.1016/j.asoc.2020.106505 Mourtaji, 2021, Hybrid rule-based solution for phishing URL detection using convolutional neural network, Wirel. Commun. Mob. Comput., 2021, 10.1155/2021/8241104 Gandotra, 2021, An efficient approach for phishing detection using machine learning, 239 Wazirali, 2021, Sustaining accurate detection of phishing URLs using SDN and feature selection approaches, Comput. Netw., 201, 10.1016/j.comnet.2021.108591 El Aassal, 2020, An in-depth benchmarking and evaluation of phishing detection research for security needs, IEEE Access, 8, 22170, 10.1109/ACCESS.2020.2969780 Dou, 2017, Systematization of knowledge (sok): A systematic review of software-based web phishing detection, IEEE Commun. Surv. Tutor., 19, 2797, 10.1109/COMST.2017.2752087 Khormali, 2021, Domain name system security and privacy: A contemporary survey, Comput. Netw., 185, 10.1016/j.comnet.2020.107699 Zuraiq, 2019, Phishing detection approaches, 1 Vrbančič, 2020, Datasets for phishing websites detection, Data Brief, 33, 10.1016/j.dib.2020.106438 2021 citizenlab, 2021 2021 2021 Team, 2021 2022 2021 2021 2022 2021 2021