Summary of Novel Interpretable and Robust Web-based Ai Platform For Phishing Email Detection, by Abdulla Al-subaiey et al.
Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection
by Abdulla Al-Subaiey, Mohammed Al-Thani, Naser Abdullah Alam, Kaniz Fatema Antora, Amith Khandakar, SM Ashfaq Uz Zaman
First submitted to arxiv on: 19 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed high-performance machine learning model for email classification boasts a f1 score of 0.99 on the largest available public dataset, making it an ideal solution for deployment in relevant applications. The model’s Explainable AI (XAI) integration enhances user trust by providing insight into the decision-making process. This research contributes to the fight against phishing by offering a practical and highly accurate real-time web-based application for phishing email detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Phishing emails are a significant threat that cause financial losses and security breaches. A new study proposes a machine learning model to help detect these emails. The model is very good at classifying emails, with an accuracy rate of almost 99%. It also includes Explainable AI (XAI) to make it easier for users to understand why certain emails are detected as phishing attempts. This research provides a practical solution to help fight against phishing and keep users safe online. |
Keywords
» Artificial intelligence » Classification » F1 score » Machine learning
