Abstract
Recently, machine learning has been used to detect phishing attacks. However, it is challenging to collect data, such as call recordings and text messages from potential victims, due to privacy issues. Therefore, we introduce a phishing detection technique using federated learning that can preserve user data privacy through collaborative training that keeps local user data private. To improve the detection accuracy of phishing detection, our algorithm groups clients based on their characteristics to recommend personalized data requirements. Our results show that the proposed approach can increase the attack detection accuracy by reducing the impact of data imbalance in the phishing data.
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
Gangavarapu, T., Jaidhar, C., Chanduka, B.: Applicability of machine learning in spam and phishing email filtering: review and approaches. Artif. Intell. Rev. 53(7), 5019–5081 (2020)
Basnet, R.B., Doleck, T.: Towards developing a tool to detect phishing URLs: a machine learning approach. In: Proceedings of the IEEE Conference on Computational Intelligence and Communication, pp. 220–223 (2015)
Thapa, C., et al.: Evaluation of federated learning in phishing email detection. arXiv:2007.13300 (2020)
Brendan McMahan, H., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) (2017)
Milandu, K.M..B., Park, D.J.: A real-time efficient detection technique of voice phishing with AI. In: Proceedings of the Korean Information Science Society Conference, pp. 768–770 (2021)
Park, E.L., Cho, S.: KoNLPy: Korean natural language processing in Python. In: Proceedings of the 26th Annual Conference on Human & Cognitive Language Technology, Chuncheon, Korea (2014)
Duan, M., Liu, D., Chen, X., Liu, R., Tan, Y.: Self-balancing federated learning with global imbalanced data in mobile systems. IEEE Trans. Parallel Distrib. Syst. 32(1), 59–71 (2021)
Flower Summit 2022, 16 September 2022. https://flower.dev/conf/flower-summit-2022/. Accessed 20 Sept 2022
Hanzely, F., Zhao, B., Kolar, M.: Personalized federated learning: a unified framework and universal optimization technique. In: Proceedings of International Conference on Learning Representations (ICLR) (2021)
Anaissi, A., Suleiman, B.: A personalized federated learning algorithm: an application in anomaly detection. arXiv preprint arXiv:2111.02627 (2021)
Acknowledgement
This research was supported by the MSIT Korea under the NRF Korea (NRF-2022R1A2C4001270), by the MSIT Korea under the India-Korea Joint Programme of Cooperation in Science & Technology (NRF-2020K1A3A1A68093469), and by the ITRC program (IITP-2020-2020-0-01602) supervised by the IITP.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yoon, J.Y., Choi, B.J. (2023). Privacy-Friendly Phishing Attack Detection Using Personalized Federated Learning. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_46
Download citation
DOI: https://doi.org/10.1007/978-3-031-27199-1_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27198-4
Online ISBN: 978-3-031-27199-1
eBook Packages: Computer ScienceComputer Science (R0)