Abstract
A malicious website, also known as a phishing website, remains one of the major concerns in the cybersecurity domain. Among numerous deep learning-based solutions for phishing website detection, a Convolutional Neural Network (CNN) is one of the most popular techniques. However, when used as a stand-alone classifier, CNN still suffers from an accuracy deficiency issue. Therefore, the main objective of this paper is to explore the hybridization of CNN with another deep learning algorithm to address this problem. In this study, CNN was combined with Bidirectional Gated Recurrent Unit (BiGRU) to construct an ensemble model for malicious webpage classification. The performance of the proposed CNN-BiGRU model was evaluated against several deep learning approaches using the same dataset. The results indicated that the proposed CNN-BiGRU is a promising solution for malicious website detection. In addition, ensemble architectures outperformed single models as they joined the advantages and cured the disadvantages of individual deep learning algorithms.
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References
Wei, W., Ke, Q., Nowak, J., Korytkowski, M., Scherer, R., Woźniak, M.: Accurate and fast URL phishing detector: a convolutional neural network approach. Comput. Netw. 178 (2020). https://doi.org/10.1016/j.comnet.2020.107275
Feng, J., Zou, L., Yang, Y., Han, O., Zhou, J.: Web2Vec: phishing webpage detection method based on multidimensional features driven by deep learning. IEEE Access. 8, (2020). https://doi.org/10.1109/ACCESS.2020.3043188
Xiao, X., Zhang, D., Hu, G., Jiang, Y., Xia, S.: CNN–MHSA: a Convolutional Neural Network and multi-head self-attention combined approach for detecting phishing websites. Neural Netw. 125, 303–312 (2020). https://doi.org/10.1016/j.neunet.2020.02.013
Adebowale, M.A., Lwin, K.T., Hossain, M.A.: Intelligent phishing detection scheme using deep learning algorithms. J. Enterp. Inf. Manag. (2020). https://doi.org/10.1108/JEIM-01-2020-0036
Liu, D., Lee, J., Wang, W., Wang, Y.: Malicious Websites Detection via CNN based Screenshot Recognition*. 115–119 (2019)
Huang, Y., Yang, Q., Qin, J., Wen, W.: Phishing URL detection via CNN and attention-based hierarchical RNN. Proc. - 2019 18th IEEE Int. Conf. Trust. Secur. Priv. Comput. Commun. IEEE Int. Conf. Big Data Sci. Eng. Trust. 112–119 (2019). https://doi.org/10.1109/TrustCom/BigDataSE.2019.00024
Al-Ahmadi, S., Alharbi, Y.: A deep learning technique for web phishing detection combined URL features and visual similarity. Int. J. Comput. Netw. Commun. 12, 41–54 (2020). https://doi.org/10.5121/ijcnc.2020.12503
Srinivasan, S., Vidyapeetham, A.V., Ravi, V., Arunachalam, A., Universitet, O., Alazab, M.: Malware analysis using artificial intelligence and deep learning. Malware Anal. Using Artif. Intell. Deep Learn. (2021). https://doi.org/10.1007/978-3-030-62582-5
Rasymas, T., Dovydaitis, L.: Detection of phishing URLs by using deep learning approach and multiple features combinations. Balt. J. Mod. Comput. 8, 471–483 (2020). https://doi.org/10.22364/BJMC.2020.8.3.06
Yuan, L., Zeng, Z., Lu, Y., Ou, X., Feng, T.: A character-level bigru-attention for phishing classification. In: Zhou, J., Luo, X., Shen, Q., Xu, Z. (eds.) ICICS 2019. LNCS, vol. 11999, pp. 746–762. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-41579-2_43
Ozcan, A., Catal, C., Donmez, E., Senturk, B.: A hybrid DNN–LSTM model for detecting phishing URLs. Neural Comput. Appl. (2021)https://doi.org/10.1007/s00521-021-06401-z
Quang, D.N., Selamat, A., Krejcar, O.: Recent research on phishing detection through machine learning algorithm. In: Fujita, H., Selamat, A., Lin, J.-W., Ali, M. (eds.) IEA/AIE 2021. LNCS (LNAI), vol. 12798, pp. 495–508. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79457-6_42
Do, N.Q., Selamat, A., Krejcar, O., Yokoi, T., Fujita, H.: Phishing webpage classification via deep learning‐based algorithms: an empirical study. Appl. Sci. 11 (2021). https://doi.org/10.3390/app11199210
Acknowledgement
The authors sincerely thank Universiti Teknologi Malaysia (UTM) under Malaysia Research University Network (MRUN) Vot 4L876, for the completion of the research. This work was also partially supported/funded by the Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1) and Universiti Tenaga Nasional (UNITEN). The work and the contribution were also supported by the SPEV project “Smart Solutions in Ubiquitous Computing Environments”, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-SPEV-2022–2102). We are also grateful for the support of student Michal Dobrovolny in consultations regarding application aspects.
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Do, N.Q., Selamat, A., Lim, K.C., Krejcar, O. (2022). An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_42
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