Detecting Phishing Domains Using Machine Learning
Source abstract
Abstract
Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. These models were developed using artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and random forest (RF) techniques. Moreover, the uniform resource locator’s (URL’s) UCI phishing domains dataset is used as a benchmark to evaluate the models. Our findings show that the model based on the random forest technique is the most accurate of the other four techniques and outperforms other solutions in the literature.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Spam and Phishing Detection · Network Security and Intrusion Detection · Advanced Malware Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Collage of Computer and Information Technology, Taif University, Taif 26571, Saudi Arabia
Funding
Deanship of Scientific Research, Taif University
References (71)
Show source-provided references
1. Cybersecurity Education: Evolution of the Discipline and Analysis of Master Programs10.1016/j.cose.2018.01.015 ↗
2. KeySplitWatermark: Zero Watermarking Algorithm for Software Protection Against Cyber-Attacks10.1109/ACCESS.2020.2988160 ↗
3. Ensemble Adaboost Classifier for Accurate and Fast Detection of Botnet Attacks in Connected Vehicles
4. Conklin, W.A., Cline, R.E., and Roosa, T. (2014, January 6–9). Re-Engineering Cybersecurity Education in the US: An Analysis of the Critical Factors. Proceedings of the 2014 47th Hawaii International Conference on System Sciences, IEEE, Waikoloa, HI, USA.10.1109/HICSS.2014.254 ↗
5. Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network10.1109/TITS.2020.3025875 ↗
6. Analysis of Security and Energy Efficiency for Shortest Route Discovery in Low-energy Adaptive Clustering Hierarchy Protocol Using Levenberg-Marquardt Neural Network and Gated Recurrent Unit for Intrusion Detection System10.1002/ett.3997 ↗
7. Bleau, H., and Global Fraud and Cybercrime Forecast (2021, November 19). Retrieved RSA 2017. Available online: https://www.rsa.com/en-us/resources/2017-global-fraud.
8. Computer Fraud & Security (2019). APWG: Phishing Activity Trends Report Q4 2018. Comput. Fraud Secur., 2019, 4.10.1016/S1361-3723(19)30025-9 ↗
9. Hulten, G.J., Rehfuss, P.S., Rounthwaite, R., Goodman, J.T., Seshadrinathan, G., Penta, A.P., Mishra, M., Deyo, R.C., Haber, E.J., and Snelling, D.A.W. (2014). Finding Phishing Sites, Google Patents.
10. (2021, November 20). What Is Phishing and How to Spot a Potential Phishing Attack. PsycEXTRA Dataset. Available online: https://www.imperva.com/learn/application-security/phishing-attack-scam/.
11. Fighting against Phishing Attacks: State of the Art and Future Challenges10.1007/s00521-016-2275-y ↗
12. DTOF-ANN: An Artificial Neural Network Phishing Detection Model Based on Decision Tree and Optimal Features10.1016/j.asoc.2020.106505 ↗
13. (2021, November 25). Machine Learning Decision Tree Classification Algorithm—Javatpoint. Available online: https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm.
14. Random Forests10.1023/A:1010933404324 ↗
15. Friedman, J.H. (2017). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Open.
16. Brownlee, J. (2020). Train-Test Split for Evaluating Machine Learning Algorithms. Mach. Learn. Mastery, 23, Available online: https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/.
17. Subasi, A., Molah, E., Almkallawi, F., and Chaudhery, T.J. (2017, January 21–23). Intelligent Phishing Website Detection Using Random Forest Classifier. Proceedings of the 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA), Ras Al Khaimah, United Arab Emirates.10.1109/ICECTA.2017.8252051 ↗
18. (2021, December 27). Jeremybeauchamp English: A Visual Comparison between the Complexity of Decision Trees and Random Forests. Available online: https://commons.wikimedia.org/wiki/File:Decision_Tree_vs._Random_Forest.png.
19. Sönmez, Y., Tuncer, T., Gökal, H., and Avcı, E. (2018, January 22–25). Phishing Web Sites Features Classification Based on Extreme Learning Machine. Proceedings of the 2018 6th International Symposium on Digital Forensic and Security (ISDFS), IEEE, Antalya, Turkey.10.1109/ISDFS.2018.8355342 ↗
20. ResearchGate (2021, October 06). Figure 2. Classification of Data by Support Vector Machine (SVM). Available online: https://www.researchgate.net/figure/Classification-of-data-by-support-vector-machine-SVM_fig8_304611323.
21. Cristianini, N., and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press.10.1017/CBO9780511801389 ↗
22. A Survey on Ensemble Learning for Data Stream Classification
23. Zhou, Z.-H. (2019). Ensemble Methods: Foundations and Algorithms, Chapman and Hall/CRC.
24. Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification10.1155/2019/9152506 ↗
25. (2021, October 06). Bagging (Bootstrap Aggregation)—Overview, How It Works, Advantages—Ro.Outletshop2021.Ru. Available online: https://corporatefinanceinstitute.com/resources/data-science/bagging-bootstrap-aggregation/#:~:text=Bagging%20offers%20the%20advantage%20of,of%20interpretability%20of%20a%20model.
26. An Iterative Boosting-Based Ensemble for Streaming Data Classification10.1016/j.inffus.2018.01.003 ↗
27. Zhou, Z.-H. (2021). Machine Learning, Springer.10.1007/978-981-15-1967-3 ↗
28. (2021, October 06). AdaBoost Classifier in Python—DataCamp. Available online: https://www.datacamp.com/tutorial/adaboost-classifier-python.
29. State-of-the-Art in Artificial Neural Network Applications: A Survey10.1016/j.heliyon.2018.e00938 ↗
30. A Logical Calculus of the Ideas Immanent in Nervous Activity10.1007/BF02478259 ↗
31. Analysis of Bacterial Community in Bulking Sludge Using Culture-Dependent and-Independent Approaches10.1016/S1001-0742(10)60621-3 ↗
32. Artificial Neural Network Modeling of Biosorption Process Using Agricultural Wastes in a Rotating Packed Bed10.1016/j.applthermaleng.2018.05.029 ↗
33. Artificial Neural Network Modelling of the Amount of Separately-Collected Household Packaging Waste10.1016/j.jclepro.2018.11.063 ↗
34. A Comprehensive Survey of AI-Enabled Phishing attacks detection techniques10.1007/s11235-020-00733-2 ↗
35. (2018). A Comprehensive Guide to Understand and Implement Text Classification in Python. Anal. Vidhya, Available online: http://www.shivambansal.com/blog/text-classification-guide/.
36. Phishing URL Detection: A Real-Case Scenario Through Login URLs10.1109/ACCESS.2022.3168681 ↗
37. James, J., Sandhya, L., and Thomas, C. (2013, January 13–15). Detection of Phishing URLs Using Machine Learning Techniques. Proceedings of the 2013 International Conference on Control Communication and Computing (ICCC), Thiruvananthapuram, India. Available online: https://ieeexplore.ieee.org/abstract/document/6731669.10.1109/ICCC.2013.6731669 ↗
38. An Effective Security Alert Mechanism for Real-Time Phishing Tweet Detection on Twitter—ScienceDirect10.1016/j.cose.2019.02.004 ↗
39. Meng, L., and Zhang, Y. (2018, January 6–8). Detecting Phishing Websites with Random Forest. Proceedings of the Machine Learning and Intelligent Communications, Hangzhou, China.
40. Patil, V., Thakkar, P., Shah, C., Bhat, T., and Godse, S.P. (2018, January 19–18). Detection and Prevention of Phishing Websites Using Machine Learning Approach. Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India.10.1109/ICCUBEA.2018.8697412 ↗
41. Joshi, A., and Pattanshetti, P.T.R. (2019). Phishing Attack Detection Using Feature Selection Techniques, Social Science Research Network.10.2139/ssrn.3418542 ↗
42. Phishing Website Detection: An Improved Accuracy through Feature Selection and Ensemble Learning
43. A Stacking Model Using URL and HTML Features for Phishing Webpage Detection10.1016/j.future.2018.11.004 ↗
44. Phishing Web Site Detection Using Diverse Machine Learning Algorithms10.1108/EL-05-2019-0118 ↗
45. AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites10.1109/ACCESS.2020.3013699 ↗
46. Particle Swarm Optimization-Based Feature Weighting for Improving Intelligent Phishing Website Detection10.1109/ACCESS.2020.3003569 ↗
47. Intelligent Web-Phishing Detection and Protection Scheme Using Integrated Features of Images, Frames and Text—ScienceDirect10.1016/j.eswa.2018.07.067 ↗
48. An In-Depth Benchmarking and Evaluation of Phishing Detection Research for Security Needs10.1109/ACCESS.2020.2969780 ↗
49. Comparison of Adaboost with MultiBoosting for Phishing Website Detection—ScienceDirect10.1016/j.procs.2020.02.251 ↗
50. Phishing Page Detection via Learning Classifiers from Page Layout Feature10.1186/s13638-019-1361-0 ↗
51. (2021, September 27). A Novel Machine Learning Approach to Detect Phishing Websites. Available online: https://ieeexplore.ieee.org/abstract/document/8474040/.
52. AI@ntiPhish—Machine Learning Mechanisms for Cyber-Phishing Attack10.1587/transinf.2018NTI0001 ↗
53. Abdelhamid, N., Thabtah, F., and Abdel-Jaber, H. (2017, January 22–24). Phishing Detection: A Recent Intelligent Machine Learning Comparison Based on Models Content and Features. Proceedings of the 2017 IEEE International Conference on Intelligence and Security Informatics, Beijing, China. Available online: https://ieeexplore.ieee.org/abstract/document/8004877.10.1109/ISI.2017.8004877 ↗
54. Towards Detection of Phishing Websites on Client-Side Using Machine Learning Based Approach10.1007/s11235-017-0414-0 ↗
55. Smart Phishing Detection in Web Pages Using Supervised Deep Learning Classification and Optimization Technique ADAM10.1007/s11277-021-08196-7 ↗
56. Machine Learning Based Phishing Detection from URLs—ScienceDirect10.1016/j.eswa.2018.09.029 ↗
57. URL Phishing Analysis Using Random Forest
58. Niranjan, A., Haripriya, D.K., Pooja, R., Sarah, S., Deepa Shenoy, P., and Venugopal, K.R. (2019). EKRV: Ensemble of KNN and Random Committee Using Voting for Efficient Classification of Phishing, Springer. Available online: https://link.springer.com/chapter/10.1007/978-981-13-1708-8_37.10.1007/978-981-13-1708-8_37 ↗
59. A New Hybrid Ensemble Feature Selection Framework for Machine Learning-Based Phishing Detection System—ScienceDirect10.1016/j.ins.2019.01.064 ↗
60. Pandey, A., Gill, N., Sai Prasad Nadendla, K., and Thaseen, I.S. (2018, January 6–8). Identification of Phishing Attack in Websites Using Random Forest-SVM Hybrid Model. Proceedings of the Intelligent Systems Design and Applications: 18th International Conference on Intelligent Systems Design and Applications (ISDA 2018), Vellore, India. Available online: https://link.springer.com/chapter/10.1007/978-3-030-16660-1_12.
61. Hybrid Intelligent Phishing Website Prediction Using Deep Neural Networks with Genetic Algorithm-Based Feature Selection and Weighting10.1049/iet-ifs.2019.0006 ↗
62. Aljofey, A., Jiang, Q., Qu, Q., Huang, M., and Niyigena, J.P. (2020). An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL. Electronics, 9, Available online: https://www.mdpi.com/2079-9292/9/9/1514.10.3390/electronics9091514 ↗
63. Critical Analysis of Current Research Aimed at Improving Detection of Phishing Attacks
64. Deep Learning to Combat Phishing
65. Detecting Phishing Websites via Aggregation Analysis of Page Layouts—ScienceDirect10.1016/j.procs.2018.03.053 ↗
66. An Improved ELM-Based and Data Preprocessing Integrated Approach for Phishing Detection Considering Comprehensive Features—ScienceDirect10.1016/j.eswa.2020.113863 ↗
67. Phishing Website Detection Using Support Vector Machines and Nature-Inspired Optimization Algorithms10.1007/s11235-020-00739-w ↗
68. (2021, November 29). UCI Machine Learning Repository: Phishing Websites Data Set. Available online: https://archive.ics.uci.edu/ml/datasets/phishing+websites.
69. An Efficacious Method for Detecting Phishing Webpages through Target Domain Identification10.1016/j.dss.2014.01.002 ↗
70. Singh, C. (2020, January 6–7). Phishing Website Detection Based on Machine Learning: A Survey. Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, Coimbatore, India.10.1109/ICACCS48705.2020.9074400 ↗
71. Phishing Website Detection: Forest by Penalizing Attributes Algorithm and Its Enhanced Variations10.1007/s13369-020-04802-1 ↗