TrustShield AI: Hybrid ML-Based Phishing Detection using Flask, scikit-learn & MongoDB
📰 Dev.to · Yadagani Sai Tejus
Learn to build a hybrid ML-based phishing detection system using Flask, scikit-learn, and MongoDB to combat increasingly sophisticated phishing emails
Action Steps
- Build a phishing detection model using scikit-learn and train it on a labeled dataset
- Configure a Flask API to receive email data and send it to the detection model
- Integrate MongoDB to store and manage email data and model outputs
- Test the system using a sample dataset and evaluate its performance
- Deploy the system to a production environment and monitor its effectiveness
Who Needs to Know This
This project benefits developers and data scientists on a team who want to improve email security and protect users from phishing attacks. It requires collaboration between backend developers, data scientists, and cybersecurity experts.
Key Insight
💡 Hybrid ML-based phishing detection systems can effectively combat sophisticated phishing emails by combining machine learning algorithms with expert knowledge
Share This
🚨 Protect your users from phishing emails with TrustShield AI, a hybrid ML-based detection system 🚨
Key Takeaways
Learn to build a hybrid ML-based phishing detection system using Flask, scikit-learn, and MongoDB to combat increasingly sophisticated phishing emails
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The Problem That Made Us Build This Phishing emails are getting much harder to...
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