Abstract
Phishing is the technique by which the attacker tries to obtain confidential information from the user, with the purpose of using it fraudulently. These days, three ways to mitigate such attacks stand out: Focus based on awareness, based on blacklists, and based on machine learning (ML). However, in the last days, Deep Learning (DL) has emerged as one of the most efficient techniques of machine learning. Thus, this systematic literature review has been aimed to offer to other researchers, readers and users, an analysis of a variety of proposals of other researchers how to face these attacks, applying Deep Learning algorithms. Some of the contributions of the current study include a synthesis of each selected work and the classification of anti-phishing solutions through its approach, obtaining that the uniform resource locator (URL)-oriented approach is the most used. Furthermore, we have been able to classify the Deep Learning algorithms selected in each solution, which yielded that the most commonly used are the deep neural network (DNN) and convolutional neural network (CNN), among other fundamental data.
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References
Hajgude, J., Ragha, L.: Phish mail guard: Phishing mail detection technique by using textual and URL analysis. In: 2012 World Congress on Information and Communication Technologies, pp. 297–302 (2012)
Marchal, S., Armano, G., Grondahl, T., Saari, K., Singh, N., Asokan, N.: Off-the-Hook: An Efficient and Usable Client-Side Phishing Prevention Application. IEEE Trans. Comput. 66(10), 1717–1733 (2017)
Whittaker, C., Ryner, B., Nazif, M.: Large-Scale Automatic Classification of Phishing Pages
Deng, L.: A Tutorial Survey of Architectures, Algorithms, and Applications for Deep Learning. APSIPA Trans. Signal Inf. Process. (2014)
Selvaganapathy, S., Nivaashini, M., Natarajan, H.: Deep belief network based detection and categorization of malicious URLs. Inf. Secur. J. A Glob. Perspect. 27(3), 145–161 (2018)
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., Linkman, S.: Systematic literature reviews in software engineering – A systematic literature review. Inf. Softw. Technol. 51(1), 7–15 (2009)
Basnet, R., Mukkamala, S., Sung, A.H.: Detection of Phishing Attacks: A Machine Learning Approach. In Soft Computing Applications in Industry, pp. 373–383. Berlin, Heidelberg, Springer Berlin Heidelberg (2008)
Yuan, X.: PhD Forum: Deep Learning-Based Real-Time Malware Detection with Multi-Stage Analysis. In 2017 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–2 (2017)
Woodbridge, J., Anderson, H.S., Ahuja, A., Endgame, D.G.: Detecting Homoglyph Attacks with a Siamese Neural Network
Saxe, J., Berlin, K.: eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys (2017)
Shima, K., et al.: Classification of URL bitstreams using Bag of Bytes (2018)
Vazhayil, A., Vinayakumar, R., Soman, K.: Comparative Study of the Detection of Malicious URLs Using Shallow and Deep Networks. In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–6 (2018)
Epishkina, A., Zapechnikov, S.: A syllabus on data mining and machine learning with applications to cybersecurity. In 2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC), pp. 194–199 (2016)
Zhang, X., Zeng, Y., Jin, X.-B., Yan, Z.-W., Geng, G.-G.: Boosting the phishing detection performance by semantic analysis. In 2017 IEEE International Conference on Big Data (Big Data), pp. 1063–1070 (2017)
Vanhoenshoven, F., Napoles, G., Falcon, R., Vanhoof, K., Koppen, M.: Detecting malicious URLs using machine learning techniques. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8 (2016)
Chen, W., Zhang, W., Su, Y.: Phishing Detection Research Based on LSTM Recurrent Neural Network, pp. 638–645. Springer, Singapore (2018)
Zhang, J., Li, X.: Phishing Detection Method Based on Borderline-Smote Deep Belief Network, pp. 45–53. Springer, Cham (2017)
Yi, P., Guan, Y., Zou, F., Yao, Y., Wang, W., Zhu, T.: Web Phishing Detection Using a Deep Learning Framework. Wirel. Commun. Mob. Comput. 2018, 1–9 (2018)
Aksu, D., Turgut, Z., Üstebay, S., Aydin, M.A.: Phishing Analysis of Websites Using Classification Techniques, pp. 251–258. Springer, Singapore (2019)
Zhao, J., Wang, N., Ma, Q., Cheng, Z.: Classifying Malicious URLs Using Gated Recurrent Neural Networks, pp. 385–394. Springer, Cham (2019)
Jiang, J., et al.: A Deep Learning Based Online Malicious URL and DNS Detection Scheme, pp. 438–448. Springer, Cham (2018)
Spaulding, J., Mohaisen, A.: Defending Internet of Things Against Malicious Domain Names using D-FENS. In 2018 IEEE/ACM Symposium on Edge Computing (SEC), pp. 387–392 (2018)
Pereira, M., Coleman, S., Yu, B., DeCock, M., Nascimento, A.: Dictionary Extraction and Detection of Algorithmically Generated Domain Names in Passive DNS Traffic, pp. 295–314. Springer, Cham (2018)
Rao, R.S., Pais, A.R.: Detection of phishing websites using an efficient feature-based machine learning framework. Neural Comput. Appl., 1–23 (2018)
Sur, C.: DeepSeq: learning browsing log data based personalized security vulnerabilities and counter intelligent measures. J. Ambient Intell. Humaniz. Comput., 1–30 (2018)
Vrbančič, G., Fister, I., Podgorelec, V.: Swarm Intelligence Approaches for Parameter Setting of Deep Learning Neural Network. In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics—WIMS ’18, pp. 1–8 (2018)
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Benavides, E., Fuertes, W., Sanchez, S., Sanchez, M. (2020). Classification of Phishing Attack Solutions by Employing Deep Learning Techniques: A Systematic Literature Review. In: Rocha, Á., Pereira, R. (eds) Developments and Advances in Defense and Security. Smart Innovation, Systems and Technologies, vol 152. Springer, Singapore. https://doi.org/10.1007/978-981-13-9155-2_5
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DOI: https://doi.org/10.1007/978-981-13-9155-2_5
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