- Title
- Spam email categorization with nlp and using federated deep learning
- Creator
- Ul Haq, Ikram; Black, Paul; Gondal, Iqbal; Kamruzzaman, Joarder; Watters, Paul; Kayes, A.
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/190671
- Identifier
- vital:17690
- Identifier
-
https://doi.org/10.1007/978-3-031-22137-8_2
- Identifier
- ISBN:0302-9743 (ISSN); 9783031221361 (ISBN)
- Abstract
- Emails are the most popular and efficient communication method that makes them vulnerable to misuse. Federated learning (FL) provides a decentralized machine learning (ML) model, where a central server coordinates clients that collaboratively train a shared ML model. This paper proposes Federated Phishing Filtering (FPF) technique based on federated learning, natural language processing, and deep learning. FL for intelligent algorithms fuses trained models of ML algorithms from multiple sites for collective learning. This approach improves ML performance by utilizing large collective training data sets across the corporate client base, resulting in higher phishing email detection accuracy. FPF techniques preserve email privacy using local feature extraction on client email servers. Thus, the contents of emails do not need to be transmitted across the network or stored on third-party servers. We have applied FL and Natural Language Processing (NLP) for email phishing detection. This technique provides four training modes that perform FL without sharing email content. Our research categorizes emails as benign, spam, and phishing. Empirical evaluations with publicly available datasets show that accuracy is improved by the use of our Federated Deep Learning model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- 18th International Conference on Advanced Data Mining and Applications, ADMA 2022, Brisbane, Australia, 28-30 November 2022, Advanced Data Mining and Applications, 18th International Conference, ADMA 2022 Vol. 13726 LNAI, p. 15-27
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022, The Author(s)
- Subject
- Deep learning; Federated learning; Incremental learning; Model averaging; Phishing detection; Privacy-preserving; Spam detection; TF/IDF
- Reviewed
- Funder
- Oceania Cyber Security
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