Phishing Email Detection System Based on Scikit-learn: Technical Implementation and Security Protection

This article introduces a machine learning project for phishing email detection built using Scikit-learn, analyzing its technical architecture, feature engineering methods, and practical application value. The system aims to address complex phishing attacks that are difficult to handle with traditional rule-based protection methods, identifying subtle anomalies through machine learning to improve the accuracy of threat detection.

In the digital transformation era, email has become the main vector for attacks, with over 90% of cyberattacks starting from phishing emails. Traditional rules (blacklists, keyword matching) struggle to deal with modern phishing techniques (social engineering, domain spoofing, AI-generated content). Machine learning-based intelligent detection systems have become a key defense, as they can learn pattern features from historical data.

The project uses a Python toolchain with Scikit-learn as the core dependency. The workflow includes: data collection (public datasets such as the Enron Email Dataset), cleaning (removing HTML tags, handling encoding), feature engineering (text features like TF-IDF, metadata like sender domain and link information), and model selection (Naive Bayes, SVM, ensemble methods, etc., considering metrics such as accuracy and recall).

Feature design integrates technology and experience: text level (urgent vocabulary, spelling errors, vague references); URL analysis (shortening services, similar domains, complex structures); email headers (missing SPF/DKIM/DMARC, inconsistent addresses); visual presentation (HTML template flaws, DOM structure features).

The training phase requires reasonable data partitioning (time series/stratified sampling) and handling class imbalance (under/over-sampling, weight adjustment). Evaluation uses metrics such as precision, recall, F1-score, and ROC-AUC. Cross-validation ensures generalization ability, while Scikit-learn feature importance and tools like LIME/SHAP enhance interpretability.

Deployment requires balancing model complexity and inference latency (optimization via serialization and ONNX conversion). Continuous maintenance includes regular retraining, performance monitoring, and drift detection. Integration with existing security facilities (gateways, SIEM) is needed, along with designing standard APIs and establishing mechanisms for false positive appeals and false negative feedback.

This project demonstrates the practical value of ML in cybersecurity. Future directions include deep learning (Transformers), multimodal fusion, and federated learning. It is recommended that developers start by understanding the essence of phishing, master text processing and feature engineering skills, and participate in open-source projects and community exchanges.