Abstract
Phishing attacks are the most frequently used method for attackers to obtain sensitive information from victims or infect their networks, as the number of phishing attacks continues to grow rapidly due to their simplicity and low cost of distribution, as well as the appearance of phishing-as-a-service. Thus, phishing email detection is a critical issue that requires immediate attention, where we have focused on resolving the phishing email detection problem using only email bodies. The current study proposed and trained a model using Bi-GRU and two dimensional CNN, in which words are represented using pre-trained GloVe word embeddings. The experimental results show that our model has achieved 98.44% precision, which shows the effectiveness of our model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Protecting businesses against cyber threats during COVID-19 and beyond. https://cloud.google.com/blog/products/identity-security/protecting-against-cyber-threats-during-covid-19-and-beyond. Accessed 12 Aug 2021
Verizon. 2021 Data Breach Investigations Report 1st Quarter 2021 (2021). https://enterprise.verizon.com/resources/reports/2021-dbir-executive-brief.pdf. Accessed 12 Aug 2021
Anti-Phishing Working Group. Phishing Activity Trends Report 1st Quarter 2021 (2021). https://docs.apwg.org/reports/apwg_trends_report_q1_2021.pdf. Accessed 12 Aug 2021
Sahoo, D., Liu, C., Hoi, S.C.: Malicious URL detection using machine learning: a survey. arXiv preprint arXiv:1701.07179 (2017)
Ding, Y., Luktarhan, N., Li, K., Slamu, W.: A keyword-based combination approach for detecting phishing webpages. Comput. Secur. 84, 256–275 (2019)
Fang, Y., Zhang, C., Huang, C., Liu, L., Yang, Y.: Phishing email detection using improved RCNN model with multilevel vectors and attention mechanism. IEEE Access 7, 56329–56340 (2019). https://doi.org/10.1109/ACCESS.2019.2913705
Zhou, P., Qi, Z., Zheng, S., Xu, J., Bao, H., Xu, B.: Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling. arXiv preprint arXiv:1611.06639 (2016)
Tajaddodianfar, F., Stokes, J.W., Gururajan, A.: Texception: a character/word-level deep learning model for phishing URL detection. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2857–2861. IEEE (2020)
Prakash, P., Kumar, M., Kompella, R.R., Gupta, M.: PhishNet: predictive blacklisting to detect phishing attacks. In: 2010 Proceedings IEEE INFOCOM, pp. 1–5. IEEE (2010)
Rao, R.S., Pais, A.R.: An enhanced blacklist method to detect phishing websites. In: Shyamasundar, R.K., Singh, V., Vaidya, J. (eds.) ICISS 2017. LNCS, vol. 10717, pp. 323–333. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-72598-7_20
Zamir, A., et al.: Phishing web site detection using diverse machine learning algorithms. The Electronic Library ahead-of-print (2020). https://doi.org/10.1108/EL-05-2019-0118
Alqahtani, H., Sarker, I.H., Kalim, A., Minhaz Hossain, S.M., Ikhlaq, S., Hossain, S.: Cyber intrusion detection using machine learning classification techniques. In: Chaubey, N., Parikh, S., Amin, K. (eds.) COMS2 2020. CCIS, vol. 1235, pp. 121–131. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-6648-6_10
Akinyelu, A.A., Adewumi, A.O.: Classification of phishing email using random forest machine learning technique. J. Appl. Math. (2014)
Figueroa, N., L’huillier, G., Weber, R.: Adversarial classification using signaling games with an application to phishing detection. Data Min. Knowl. Discov. 31(1), 92–133 (2017). https://doi.org/10.1007/s10618-016-0459-9
Nguyen, M., Nguyen, T., Nguyen, T.H.: A deep learning model with hierarchical LSTMs and supervised attention for anti-phishing. In: CEUR Workshop Proceedings, vol. 2124, pp. 29–38 (2018)
Douzi, S., Amar, M., El Ouahidi, B.: Advanced phishing filter using autoencoder and denoising autoencoder. In: Proceedings of the International Conference on Big Data and Internet of Thing, pp. 125–129 (2017)
Barathi Ganesh, H.B., Vinayakumar, R., Anand Kumar, M., Soman, K.P.: Distributed representation using target classes: bag of tricks for security and privacy analytics. In: CEUR Workshop Proceedings, vol. 2124, pp. 10–15 (2018)
Li, Q., Cheng, M., Wang, J., Sun, B.: LSTM based phishing detection for big email data. IEEE Trans. Big Data 1–11 (2020). https://doi.org/10.1109/TBDATA.2020.2978915
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014). http://www.aclweb.org/anthology/D14-1162
Dey, R., Salem, F.M.: Gate-variants of gated recurrent unit (GRU) neural networks. In: 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1597–1600 (2017). https://doi.org/10.1109/MWSCAS.2017.8053243
Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: INTERSPEECH (2012)
Khan, S., Rahmani, H., Shah, S.A.A., Bennamoun, M.: A guide to convolutional neural networks for computer vision. Synth. Lect. Comput. Vis. 8(1), 1–207 (2018)
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188 (2014)
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning (2016)
Security and Privacy Analytics Anti-Phishing Shared Task (IWSPA-AP 2018). https://dasavisha.github.io/IWSPA-sharedtask/. Accessed 12 Aug 2021
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Remmide, M.A., Boumahdi, F., Boustia, N. (2022). Phishing Email Detection Using Bi-GRU-CNN Model. In: Ragab Hassen, H., Batatia, H. (eds) Proceedings of the International Conference on Applied CyberSecurity (ACS) 2021. ACS 2021. Lecture Notes in Networks and Systems, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-030-95918-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-95918-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95917-3
Online ISBN: 978-3-030-95918-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)