Abstract
Due to the growing trend of Internetization, the number of connected computers has been increasing day by day. Almost all companies are transferring their main operations from the real world to the cyberworld. Although this increases the marketplace of the firms, it also brings lots of vulnerabilities, such as cyber-attacks, especially with the anonymous structure of the Internet. Phishing is one of the popular attack types which exploits the vulnerabilities to user unawareness. There are some works in the literature that gets help from the rule-based detection systems as a static preventions mechanism, and machine learning-based systems as dynamic prevention mechanisms. In this work, we implemented a deep neural network (DNN) based phishing detection system by analyzing the URL of the suspicious websites. Although in almost all previous researches the used datasets are collected by different resources in which legitimate and phishing websites are clear, in this research, we firstly create a high-risk dataset, which contains only the suspicious websites which are reported to PhishTank website. Experimental research showed that the proposed system gives a very good level of efficiency both in accuracy and execution time manner.
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References
Proofpoint Report. https://www.proofpoint.com/au/resources/threat-reports/state-of-phish Accessed 31 Oct 2020
APWG 2020 2nd Quarter Report. https://docs.apwg.org/reports/apwg_trends_report_q2_2020.pdf. Accessed 31 Oct 2020
High Risk URL Dataset. www.kaggle.com/dataset/a38e161d237f30579f2e90d8da6bb7dc50717eff63642abdd7adfda0d02218b9 (2020)
Yang, P., Zhao, G., Zeng, P.: Phishing website detection based on multidimensional features driven by deep learning. IEEE Access https://doi.org/10.1109/access.2019.2892066 (2019)
Yi, P., Guan, Y., Zou, F., Yao, Y., Wang, W., ZhuWeb, T.: Phishing Detection Using a Deep Learning Framework, Hindawi Wireless Communications and Mobile Computing Volume 2018, Article ID 4678746, (2018)
Le, H., Pham, Q., Sahoo, D., Hoi, S.: URLNet: learning a URL representation with deep learning for malicious url detection. In: Proceedings of ACM Conference, Washington, DC, USA, July 2017 (Conference 2017) (2018)
Yuan, H., Yang, Z., Chen, X., Li, Y., Liu, W.: URL2Vec: URL modeling with character embeddings for fast and accurate phishing website detection. In: IEEE (2018)
Trevisan, M., Drago, I.: Robust URL Classification with Generative Adversarial Networks, Workshop on AI in Networks (WAIN) 2018, Toulouse (2018)
Anand, A., Gorde, K., Moniz, A., Park, N., Chakraborty, T., Chu, B.: Phishing URL detection with oversampling based on text generative adversarial networks. In: 2018 IEEE International Conference on Big Data (2018)
Buber E., Demir O., Sahingoz O.K., Feature selections for the machine learning based detection of phishing websites. In: International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, pp. 1–5 (2017)
Buber, E., Diri, B., Sahingoz, O.K.: Detecting phishing attacks from URL by using NLP techniques. In: 2017 International Conference on Computer Science and Engineering (UBMK), Antalya, pp. 337–342 (2017)
Pham, T.T.T., Hoang, V.N., Ha, T.C.: Exploring efficiency of character-level convolution neuron network and long short term memory on malicious URL detection. In: Proceedings of the 2018 VII International Conference on Network, Communication and Computing (ICNCC 2018), ACM, New York, pp. 82–86 (2018)
Shivangi, S., Debnath, P., Sajeevan, K.: Chrome extension for malicious URLs detection in social media applications using artificial neural networks and long short term memory networks. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, pp. 1993–1997 (2018)
Korkmaz, M., Sahingoz, O.K., Diri, B.: Feature selections for the classification of webpages to detect phishing attacks: a survey. In: 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara (2020)
Korkmaz, M., Sahingoz, O.K., Diri B.: Detection of phishing websites by using machine learning-based URL analysis. In: 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), India, pp. 1–7 (2020)
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Korkmaz, M., Kocyigit, E., Sahingoz, O.K., Diri, B. (2021). Deep Neural Network Based Phishing Classification on a High-Risk URL Dataset. In: Abraham, A., et al. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020). SoCPaR 2020. Advances in Intelligent Systems and Computing, vol 1383. Springer, Cham. https://doi.org/10.1007/978-3-030-73689-7_62
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DOI: https://doi.org/10.1007/978-3-030-73689-7_62
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