Abstract
In the field of information security, phishing URLs detection and prevention has recently become egregious. For detecting, phishing attacks several anti-phishing systems have already been proposed by researchers. The performance of those systems can be affected due to the lack of proper selection of machine learning classifiers along with the types of feature sets. A details investigation on machine learning classifiers (KNN, DT, SVM, RF, ERT and GBT) along with three publicly available datasets with multidimensional feature sets have been presented on this paper. The performance of the classifiers has been evaluated by confusion matrix, precision, recall, F1-score, accuracy and misclassification rate. The best output obtained from Random Forest and Extremely Randomized Tree with dataset one and three (binary class feature set) of 97% and 98% accuracy accordingly. In multiclass feature set (dataset two), Gradient Boosting Tree provides highest performance with 92% accuracy.
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References
Abutair, H.Y., Belghith, A.: Using case-based reasoning for phishing detection. Procedia Comput. Sci. 109, 281–288 (2017)
Tan, C.L., Chiew, K.L.: Phishing website detection using URL-assisted brand name weighting system. In: 2014 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 054–059. IEEE (2014)
Shirazi, H., Bezawada, B., Ray, I.: Kn0w Thy Doma1n Name: unbiased phishing detection using domain name based features. In: Proceedings of the 23rd ACM on Symposium on Access Control Models and Technologies, pp. 69–75. ACM (2018)
Hutchinson, S., Zhang, Z., Liu, Q.: Detecting phishing websites with random forest. In: International Conference on Machine Learning and Intelligent Communications, pp. 470–479. Springer, Cham (2018)
Dong, Z., Kapadia, A., Blythe, J., Camp, L.J.: Beyond the lock icon: real-time detection of phishing websites using public key certificates. In: 2015 APWG Symposium on Electronic Crime Research (eCrime), pp. 1–12. IEEE (2015)
Sahingoz, O.K., Buber, E., Demir, O., Diri, B.: Machine learning based phishing detection from URLs. Expert Syst. Appl. 117, 345–357 (2019)
Jain, A.K., Gupta, B.B.: PHISH-SAFE: URL features-based phishing detection system using machine learning. In: Cyber Security: Proceedings of CSI 2015, pp. 467–474. Springer, Singapore (2018)
Gupta, S., Singhal, A.: Dynamic classification mining techniques for predicting phishing URL. In: Soft Computing: Theories and Applications, pp. 537–546. Springer, Singapore (2018)
Parekh, S., Parikh, D., Kotak, S., Sankhe, S.: A new method for detection of phishing websites: URL detection. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 949–952. IEEE (2018)
Tan, C.L., Chiew, K.L.: Phishing webpage detection using weighted URL tokens for identity keywords retrieval. In: 9th International Conference on Robotic, Vision, Signal Processing and Power Applications, pp. 133–139. Springer, Singapore (2017)
Zouina, M., Outtaj, B.: A novel lightweight URL phishing detection system using SVM and similarity index. Human-Centric Comput. Inf. Sci. 7(1), 17 (2017)
Ahmed, A.A., Abdullah, N.A.: Real time detection of phishing websites. In: 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 1–6. IEEE (2016)
Mohammad, R.M., Thabtah, F., McCluskey, L.: Intelligent rule-based phishing websites classification. IET Inf. Secur. 8(3), 153–160. IEEE (2014)
Abdelhamid, N., Ayesh, A., Thabtah, F.: Phishing detection based associative classification data mining. Expert Syst. Appl. 41(13), 5948–5959 (2014)
Tan, C.L.: Phishing dataset for machine learning: feature evaluation. Mendeley Data, v1 (2018). http://dx.doi.org/10.17632/h3cgnj8hft.1
Thaker, M., Parikh, M., Shetty, P., Neogi, V., Jaswal, S.: Detecting phishing websites using data mining. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1876–1879. IEEE (2018)
Scikit-learn: Machine Learning in Python. https://scikit-learn.org/stable/. Last accessed 11 Nov 2018
Chiew, K.L., Tan, C.L., Wong, K., Yong, K.S., Tiong, W.K.: A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Inf. Sci. (2019)
Rahman, S.S.M.M., Rahman, M.H., Sarker, K., Rahman, M.S., Ahsan, N., Sarker, M.M.: Supervised ensemble machine learning aided performance evaluation of sentiment classification. In: Journal of Physics: Conference Series, Vol. 1060(1), p. 012036. IOP Publishing (2018)
Rahman, S.S.M.M., Saha, S.K.: StackDroid: Evaluation of a multi-level approach for detecting the malware on android using stacked generalization. In International Conference on Recent Trends in Image Processing and Pattern Recognition, pp. 611–623. Springer, Singapore (2018)
Rana, M.S., Rahman, S.S.M.M., Sung, A.H.: Evaluation of tree based machine learning classifiers for android malware detection. In International Conference on Computational Collective Intelligence, pp. 377–385. Springer, Cham (2018)
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Rahman, S.S.M.M., Rafiq, F.B., Toma, T.R., Hossain, S.S., Biplob, K.B.B. (2020). Performance Assessment of Multiple Machine Learning Classifiers for Detecting the Phishing URLs. In: Raju, K.S., Senkerik, R., Lanka, S.P., Rajagopal, V. (eds) Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-15-1097-7_25
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DOI: https://doi.org/10.1007/978-981-15-1097-7_25
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