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Computer Science > Cryptography and Security
Summary
This article proposes a decentralized phishing email detection framework called Federated Phish Bowl (FedPB) which uses 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. 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%.
Q&As
What is Federated Phish Bowl (FedPB)?
Federated Phish Bowl (FedPB) is a decentralized phishing email detection framework.
How does Federated Phish Bowl facilitate collaborative phishing detection with privacy?
Federated Phish Bowl facilitates collaborative phishing detection with privacy by devising a knowledge-sharing mechanism with federated learning (FL).
What performance can Federated Phish Bowl attain compared to a centralized phishing detector?
Federated Phish Bowl can attain a competitive performance with a centralized phishing detector, with generality to various cases of FL retaining a prediction accuracy of 83%.
What is the knowledge-sharing mechanism used in Federated Phish Bowl?
The knowledge-sharing mechanism used in Federated Phish Bowl is a global word embedding matrix across the clients, with each client running its local model with Non-IID data.
What methods are used to study the effectiveness of Federated Phish Bowl?
The methods used to study the effectiveness of Federated Phish Bowl are different client numbers and data distributions.
AI Comments
👍 This article presents a new innovative framework for decentralized phishing email detection, with a competitive performance to traditional models. The authors have also provided useful links and tools to facilitate further exploration of the topic.
👎 This article lacks a thorough evaluation of the proposed framework's performance under different data distributions. Additionally, the article does not discuss any potential limitations or drawbacks of the proposed framework.
AI Discussion
Me: It's about a new decentralized phishing email detection system called Federated Phish Bowl. It uses a knowledge-sharing mechanism with federated learning and long short-term memory (LSTM) for phishing detection. It adapts by sharing a global word embedding matrix across the clients, with each client running its local model with Non-IID data.
Friend: Wow, that's really interesting. What are the implications of this article?
Me: The implications of this article is that it provides a safe and secure way to detect phishing emails without compromising confidential information. It also provides a way to collaborate with different clients to detect phishing emails with accuracy. This could help to protect people from falling victim to phishing scams.
Action items
- Research more about Federated Learning and Long Short-Term Memory (LSTM) to understand the proposed method.
- Explore the available resources such as NASA ADS, Google Scholar, Semantic Scholar, and DBLP - CS Bibliography to gain more insights into the topic.
- Experiment with the arXivLabs projects to gain hands-on experience with the proposed method.
Technical terms
- Federated Learning (FL)
- A type of machine learning that allows multiple parties to collaboratively train a model without sharing their data.
- Long Short-Term Memory (LSTM)
- A type of recurrent neural network used for natural language processing (NLP) tasks.
- Natural Language Processing (NLP)
- A field of artificial intelligence that deals with analyzing, understanding, and generating human language.
- Heuristics-based Algorithms
- Algorithms that use a set of rules or guidelines to solve a problem.