Assessment of Phishing Websites Prediction using Machine Learning Approaches
DOI:
https://doi.org/10.26438/ijcse/v12i2.3745Keywords:
Phishing Websites, Machine Learning, AI, Accuracy, Precision, Error rateAbstract
Phishing is a kind of cyberattack in which victims are tricked into divulging private information, including credit card numbers or passwords, by means of phoney emails or websites. Users may find it challenging to distinguish phishing websites from authentic websites due to their convincing appearance. This can lead to users entering their personal information on the phishing website, which can then be stolen by the attacker. An artificial intelligence technique called machine learning is used to train algorithms to find patterns in data. This can be used to create systems that automatically detect and alert users to potentially harmful websites, such as phishing website detection systems. The field of phishing website prediction currently faces some obstacles that require attention. The constant growth of phishing methods is one challenge. Artificial intelligence-based deep learning and machine learning techniques can identify phishing websites. Using machine learning techniques to predict phishing websites, we identify, monitor, and shield end users from monitoring based on phishing algorithms with respect to different publications. We present a machine learning method for phishing website identification in this research. Our method makes use of a number of characteristics, such as the URL structure, website content, and the existence of particular keywords or patterns, to discern between authentic and phishing websites. We test our method on a dataset of actual phishing websites, such as Google`s PhishCorp, Kaggle, and PhishTank, and we obtain a greater accuracy than the earlier studies on the detection of phishing websites. Our results show that machine learning can be an effective method for spotting phishing websites. With a better prototype and increased accuracy, our method is simple to use and can shield users from phishing assaults.
References
[1] F. Yahva et al., “Detection of Phising Websites using Machine Learning Approaches,” 2021 Int. Conf. Data Sci. Its Appl. ICoDSA 2021, pp.40–47, 2021, doi: 10.1109/ICODSA53588.2021.9617482.
[2] S. M. Istiaque, M. T. Tahmid, A. I. Khan, Z. Al Hassan, and S. Waheed, “Artificial Intelligence Based Cybersecurity: Two-Step Suitability Test,” 2021 IEEE Int. Conf. Serv. Oper. Logist. Informatics, SOLI 2021, 2021, doi: 10.1109/SOLI54607.2021.9672437.
[3] N. Megha, K. R. Remesh Babu, and E. Sherly, “An Intelligent System for Phishing Attack Detection and Prevention,” Proc. 4th Int. Conf. Commun. Electron. Syst. ICCES 2019, Jul., pp.1577–1582, 2019. doi: 10.1109/ICCES45898.2019.9002204.
[4] H. Dao, J. Mazel, and K. Fukuda, “CNAME Cloaking-Based Tracking on the Web: Characterization, Detection, and Protection,” IEEE Trans. Netw. Serv. Manag., Sep, Vol.18, No.3, pp.3873–3888, 2021, doi: 10.1109/TNSM.2021.3072874.
[5] A. K. Dutta, “Detecting phishing websites using machine learning technique,” PLoS One, October , Vol.16, No.10, pp.1–17, 2021, doi: 10.1371/journal.pone.0258361.
[6] E. Gandotra and D. Gupta, “An Efficient Approach for Phishing Detection using Machine Learning,” pp.239–253, 2021, doi: 10.1007/978-981-15-8711-5_12.
[7] A. Joshi and P. T. R. Pattanshetti, “Phishing Attack Detection using Feature Selection Techniques,” SSRN Electron. J., Jul. 2019, doi: 10.2139/SSRN.3418542.
[8] S. Jain and C. Gupta, “A Support Vector Machine Learning Technique for Detection of Phishing Websites,” 2023 6th Int. Conf. Inf. Syst. Comput. Networks, ISCON 2023, 2023, doi: 10.1109/ISCON57294.2023.10111968.
[9] M. H. Alkawaz, S. J. Steven, A. I. Hajamydeen, and R. Ramli, “A comprehensive survey on identification and analysis of phishing website based on machine learning methods,” ISCAIE 2021 - IEEE 11th Symp. Comput. Appl. Ind. Electron., Apr., pp.82–87, 2021, doi: 10.1109/ISCAIE51753.2021.9431794.
[10] F. Salahdine, Z. El Mrabet, and N. Kaabouch, “2022 IEEE 13th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2022,” 2022 IEEE 13th Annu. Ubiquitous Comput. Electron. Mob. Commun. Conf. UEMCON 2022, 2022.
[11] R. B. Lokitha, R. R. Monisha, N. S. Neha, T. Nadu, and T. Nadu, “Phishing Detection System using Random Forest Algorithm,” vol. 8, no. 4, pp. 510–514, 2023.
[12] V. Muppavarapu, A. Rajendran, and S. K. Vasudevan, “Phishing detection using RDF and random forests,” Int. Arab J. Inf. Technol., Vol.15, No.5, pp.817–824, 2018.
[13] R. Yang, K. Zheng, B. Wu, C. Wu, and X. Wang, “Phishing website detection based on deep convolutional neural network and random forest ensemble learning,” Sensors, Vol.21, No.24, pp.1–18, 2021, doi: 10.3390/s21248281.
[14] Z. Alshingiti, R. Alaqel, J. Al-Muhtadi, Q. E. U. Haq, K. Saleem, and M. H. Faheem, “A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN,” Electron., Vol.12, No.1, pp.1–18, 2023, doi: 10.3390/electronics12010232.
[15] M. Selvakumari, M. Sowjanya, S. Das, and S. Padmavathi, “Retraction: Phishing website detection using machine learning and deep learning techniques,” J. Phys. Conf. Ser., Vol.1916, No.1, 2021, doi: 10.1088/1742-6596/1916/1/012169.
[16] G. K. Kamalam, P. Suresh, R. Nivash, A. Ramya, and G. Raviprasath, “Detection of Phishing Websites Using Machine Learning,” 2022 Int. Conf. Comput. Commun. Informatics, ICCCI 2022, no. June, 2022, doi: 10.1109/ICCCI54379.2022.9740763.
[17] I. Tyagi, J. Shad, S. Sharma, S. Gaur, and G. Kaur, “A Novel Machine Learning Approach to Detect Phishing Websites,” 2018 5th Int. Conf. Signal Process. Integr. Networks, SPIN 2018, Sep., pp.425–430, 2018, doi: 10.1109/SPIN.2018.8474040.
[18] R. Mahajan and I. Siddavatam, “Phishing Website Detection using Machine Learning Algorithms,” Int. J. Comput. Appl., Vol.181, No. 23, pp.45–47, 2018, doi: 10.5120/ijca2018918026.
[19] Shashwat Tiwari, “Phishing Dataset for Machine Learning | Kaggle,” Kaggle, 2021.
[20] “Kaggle: Your Home for Data Science.” https://www.kaggle.com/datasets/isatish/phishing-dataset-uci-ml- csv?select=uci-ml-phishing-dataset.csv (accessed Jul. 27, 2023).
[21] Z. Fan, “Detecting and Classifying Phishing Websites by Machine Learning,” Feb., pp.48–51, 2022, doi: 10.1109/ICAML54311.2021.00018.
[22] N. B. M. Noh and M. Nazmi Bin M Basri, “Phishing Website Detection Using Random Forest and Support Vector Machine: A Comparison,” 2021 2nd Int. Conf. Artif. Intell. Data Sci. AiDAS 2021, Sep. 2021, doi:10.1109/AIDAS53897.2021.9574282.
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