Computer Science > Cryptography and Security
Raw Text
v1
Title: Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection
Authors:
Yuwei Sun
Ng Chong
Hideya Ochiai
Download PDF
Abstract: With increasingly more sophisticated phishing campaigns in recent years, phishing emails lure people using more legitimate-looking personal contexts. To tackle this problem, instead of traditional heuristics-based algorithms, more adaptive detection systems such as natural language processing (NLP)-powered approaches are essential to understanding phishing text representations. Nevertheless, concerns surrounding the collection of phishing data that might cover confidential information hinder the effectiveness of model learning. We propose a decentralized phishing email detection framework called Federated Phish Bowl (FedPB) which facilitates collaborative phishing detection with privacy. In particular, we devise a knowledge-sharing mechanism with federated learning (FL). Using long short-term memory (LSTM) for phishing detection, the framework adapts by sharing a global word embedding matrix across the clients, with each client running its local model with Non-IID data. We collected the most recent phishing samples to study the effectiveness of the proposed method using different client numbers and data distributions. The results show that FedPB can attain a competitive performance with a centralized phishing detector, with generality to various cases of FL retaining a prediction accuracy of 83%.
Cryptography and Security (cs.CR)
arXiv:2110.06025 [cs.CR]
arXiv:2110.06025v2 [cs.CR]
https://doi.org/10.48550/arXiv.2110.06025
Focus to learn more
Submission history
view email
[v1]
Full-text links:
Download:
Download a PDF of the paper titled Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection, by Yuwei Sun and 2 other authors PDF
Other formats
<Â prev
|
next >
new
|
recent
|
2110
cs
cs.LG
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
DBLP - CS Bibliography
listing
bibtex
a
export BibTeX citation
Loading...
BibTeX formatted citation
×
Data provided by:
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer
What is the Explorer?
Litmaps Toggle
Litmaps
What is Litmaps?
scite.ai Toggle
scite Smart Citations
What are Smart Citations?
Code, Data and Media Associated with this Article
DagsHub Toggle
DagsHub
What is DagsHub?
Links to Code Toggle
Papers with Code
What is Papers with Code?
ScienceCast Toggle
ScienceCast
What is ScienceCast?
Demos
Replicate Toggle
Replicate
What is Replicate?
Spaces Toggle
Hugging Face Spaces
What is Spaces?
Recommenders and Search Tools
Link to Influence Flower
Influence Flower
What are Influence Flowers?
Connected Papers Toggle
Connected Papers
What is Connected Papers?
Core recommender toggle
CORE Recommender
What is CORE?
Author
Venue
Institution
Topic
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .
Which authors of this paper are endorsers?
Disable MathJax
What is MathJax?
Single Line Text
v1. Title: Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection. Authors: Yuwei Sun. Ng Chong. Hideya Ochiai. Download PDF. Abstract: With increasingly more sophisticated phishing campaigns in recent years, phishing emails lure people using more legitimate-looking personal contexts. To tackle this problem, instead of traditional heuristics-based algorithms, more adaptive detection systems such as natural language processing (NLP)-powered approaches are essential to understanding phishing text representations. Nevertheless, concerns surrounding the collection of phishing data that might cover confidential information hinder the effectiveness of model learning. We propose a decentralized phishing email detection framework called Federated Phish Bowl (FedPB) which facilitates collaborative phishing detection with privacy. In particular, we devise a knowledge-sharing mechanism with federated learning (FL). Using long short-term memory (LSTM) for phishing detection, the framework adapts by sharing a global word embedding matrix across the clients, with each client running its local model with Non-IID data. We collected the most recent phishing samples to study the effectiveness of the proposed method using different client numbers and data distributions. The results show that FedPB can attain a competitive performance with a centralized phishing detector, with generality to various cases of FL retaining a prediction accuracy of 83%. Cryptography and Security (cs.CR) arXiv:2110.06025 [cs.CR] arXiv:2110.06025v2 [cs.CR] https://doi.org/10.48550/arXiv.2110.06025. Focus to learn more. Submission history. view email. [v1] Full-text links: Download: Download a PDF of the paper titled Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection, by Yuwei Sun and 2 other authors PDF. Other formats. < prev. |. next >. new. |. recent. |. 2110. cs. cs.LG. References & Citations. NASA ADS. Google Scholar. Semantic Scholar. DBLP - CS Bibliography. listing. bibtex. a. export BibTeX citation. Loading... BibTeX formatted citation. ×. Data provided by: Bookmark. Bibliographic and Citation Tools. Bibliographic Explorer Toggle. Bibliographic Explorer. What is the Explorer? Litmaps Toggle. Litmaps. What is Litmaps? scite.ai Toggle. scite Smart Citations. What are Smart Citations? Code, Data and Media Associated with this Article. DagsHub Toggle. DagsHub. What is DagsHub? Links to Code Toggle. Papers with Code. What is Papers with Code? ScienceCast Toggle. ScienceCast. What is ScienceCast? Demos. Replicate Toggle. Replicate. What is Replicate? Spaces Toggle. Hugging Face Spaces. What is Spaces? Recommenders and Search Tools. Link to Influence Flower. Influence Flower. What are Influence Flowers? Connected Papers Toggle. Connected Papers. What is Connected Papers? Core recommender toggle. CORE Recommender. What is CORE? Author. Venue. Institution. Topic. arXivLabs: experimental projects with community collaborators. arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? Disable MathJax. What is MathJax?