Fake Websites Detection Improvement Using Multi-Layer Artificial Neural Network Classifier with Ant Lion Optimizer Algorithm
Subject Areas : ICTFarhang Padidaran Moghaddam 1 , Mahshid Sadeghi B. 2
1 - Esfarayen University of Technology
2 - -
Keywords: Phishing Attacks, Feature Selection, Ant Lion Optimization Algorithm, Fake Pages, Fake Links,
Abstract :
In phishing attacks, a fake site is forged from the main site, which looks very similar to the original one. To direct users to these sites, Phishers or online thieves usually put fake links in emails and send them to their victims, and try to deceive users with social engineering methods and persuade them to click on fake links. Phishing attacks have significant financial losses, and most attacks focus on banks and financial gateways. Machine learning methods are an effective way to detect phishing attacks, but this is subject to selecting the optimal feature. Feature selection allows only important features to be considered as learning input and reduces the detection error of phishing attacks. In the proposed method, a multilayer artificial neural network classifier is used to reduce the detection error of phishing attacks, the feature selection phase is performed by the ant lion optimization (ALO) algorithm. Evaluations and experiments on the Rami dataset, which is related to phishing, show that the proposed method has an accuracy of about 98.53% and has less error than the multilayer artificial neural network. The proposed method is more accurate in detecting phishing attacks than BPNN, SVM, NB, C4.5, RF, and kNN learning methods with feature selection mechanism by PSO algorithm.
[1] Verma, M. K., Yadav, S., Goyal, B. K., Prasad, B. R., & Agarawal, S. (2019). Phishing Website Detection Using Neural Network and Deep Belief Network. In Recent Findings in Intelligent Computing Techniques (pp. 293-300). Springer, Singapore.
[2] Tawhid, M. A., & Ibrahim, A. M. (2020). Hybrid Binary Particle Swarm Optimization and Flower Pollination Algorithm Based on Rough Set Approach for Feature Selection Problem. In Nature-Inspired Computation in Data Mining and Machine Learning (pp. 249-273). Springer, Cham.
[3] Rao, R. S., & Pais, A. R. (2017, December). An enhanced blacklist method to detect phishing websites. In International Conference on Information Systems Security (pp. 323-333). Springer, Cham.
[4] Rao, R. S., Pais, A. R., & Anand, P. (2021). A heuristic technique to detect phishing websites using TWSVM classifier. Neural Computing and Applications, 33(11), 5733-5752.
[5] Bohacik, J., Skula, I., & Zabovsky, M. (2020, September). Data Mining-Based Phishing Detection. In 2020 15th Conference on Computer Science and Information Systems (FedCSIS) (pp. 27-30). IEEE.
[6] Alhogail, A., & Alsabih, A. (2021). Applying Machine Learning and Natural Language Processing to Detect Phishing Email. Computers & Security, 102414.
[7] Lakshmi, L., Reddy, M. P., Santhaiah, C., & Reddy, U. J. (2021). Smart Phishing Detection in Web Pages using Supervised Deep Learning Classification and Optimization Technique ADAM. Wireless Personal Communications, 118(4), 3549-3564.
[8] Sahingoz, Ozgur Koray, Ebubekir Buber, Onder Demir, and Banu Diri. "Machine learning based phishing detection from URLs." Expert Systems with Applications 117 (2019): 345-357.
[9] Basit, A., Zafar, M., Liu, X., Javed, A. R., Jalil, Z., & Kifayat, K. (2021). A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommunication Systems, 76(1), 139-154.
[10] https://docs.apwg.org/reports/apwg_trends_report_q1_2021.pdf
[11] Sahingoz, O. K., Buber, E., Demir, O., & Diri, B. (2020). Machine learning based phishing detection from URLs. Expert Systems with Applications, 117, 345-357.
[12] Zabihimayvan, Mahdieh, and Derek Doran. "Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection." arXiv preprint arXiv:1903.05675 (2019).
[13] Sahingoz, Ozgur Koray, Ebubekir Buber, Onder Demir, and Banu Diri. "Machine learning based phishing detection from URLs." Expert Systems with Applications 117 (2019): 345-357.
[14] Feng, Fang, Qingguo Zhou, Zebang Shen, Xuhui Yang, Lihong Han, and JinQiang Wang. "The application of a novel neural network in the detection of phishing websites." Journal of Ambient Intelligence and Humanized Computing (2018): 1-15
[15] Rao, R. S., & Pais, A. R. Detection of phishing websites using an efficient feature-based machine learning framework. Neural Computing and Applications, 1-23.
[16] Feng, F., Zhou, Q., Shen, Z., Yang, X., Han, L., & Wang, J. (2018). The application of a novel neural network in the detection of phishing websites. Journal of Ambient Intelligence and Humanized Computing, 1-15.
[17] Mirjalili, S. (2015). The ant lion optimizer. Advances in engineering software, 83, 80-98.
[18] Mohammad, R., Thabtah, F., & McCluskey, T. L. (2015). Phishing websites dataset.
[19] Ali, W., & Malebary, S. (2020). Particle swarm optimization-based feature weighting for improving intelligent phishing website detection. IEEE Access, 8, 116766-116780.
