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

< 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?