Phishing Detection System

Inspiration

The Phishing Detection System was inspired by the urgent need to combat phishing attacks which continue to be one of the most prevalent and damaging forms of cyber threats from deceptive emails to malicious URLs, phishing attacks exploit users' trust and can lead to financial loss, identity theft and data breaches. My goal was to build a system that could proactively detect and flag phishing attempts, leveraging machine learning and real-time data to keep users safe and secure.

What It Does

Our Phishing Detection System identifies phishing attempts by analyzing URLs and emails in real time. The system can:

• Detect phishing URLs by analyzing structural patterns and attributes commonly used in malicious sites.
• Flag phishing emails using natural language processing to identify common phrases, keywords and deceptive tactics used by phishers.
• Integrate with real-time APIs to receive the latest phishing URLs, allowing for continuous model updates and improved detection accuracy.

How I Built It

1. Data Collection: I gathered phishing and legitimate datasets from multiple sources, including [Phish Tank, OpenPhishand and the UCI Phishing websites dataset for real time data, I integrated APIs from Google Safe Browsing allowing me to continuously update our training set.

2. Feature Engineering: After preprocessing, I engineered features from URLs such as URL length, special characters, SSL status and email headers such as sender's domain, keywords in subject lines. These features helped me to capture characteristics indicative of phishing.

3. Model Training: I trained several machine learning models on our dataset, including logistic regression, random forests and XGBoost for evaluation, we used accuracy, precision, recall and F1-score to assess model performance, ultimately selecting XGBoost for its strong performance in identifying phishing attempts.

4. Real-Time API Integration: Through integrating real time data sources, our system continuously pulls in recent phishing URLs and email indicators. This keeps our model current, allowing it to adapt to the latest phishing trends and techniques.

5. Deployment: The system is to be deployed on google cloud platform and I'll use Docker for containerization, making it easy to scale. A dashboard will be created to monitor real-time phishing threats and visualize detection statistics, helping administrators quickly spot trends.

Challenges I Ran Into

1. Data Quality and Consistency: Integrating data from different sources was challenging due to varying formats and quality levels. We had to develop preprocessing scripts to clean, standardize and harmonize the data for consistent use across the model.

2. Feature Extraction from Unstructured Data: Parsing and extracting useful information from unstructured text particularly email bodies and headers was time-consuming. I experimented with regular expressions and NLP techniques to accurately identify features that could indicate phishing.

3. Handling Real-Time Updates: Managing real-time data from APIs presented its own set of challenges including rate limits and duplicate data handling. Ensuring efficient processing while avoiding overloading our system required careful planning and optimization.

4. Balancing Model Performance and Generalization: It was a challenge to build a model that could generalize well to new phishing tactics without overfitting through trial and error I fine-tuned our model to strike a balance between accuracy and robustness.

Accomplishments That I was Proud Of

-High detection accuracy: Achieving high accuracy in detecting phishing URLs and emails was a significant accomplishment as it demonstrates the model’s effectiveness in identifying threats. -Real-time data integration: Successfully integrating real-time phishing data through APIs was a major milestone, allowing our system to stay updated and adapt to new phishing trends.

What I Learned

This project taught me an In-Depth Knowledge of Phishing Techniques where I learned about the various tactics used in phishing attacks which informed our feature engineering process. Machine Learning and Feature Engineering: Building effective features from URLs and emails is essential for phishing detection and this project strengthened my skills in feature extraction and selection. Real-Time System Desig whereby integrating live data sources presented a unique challenges and taught me valuable lessons about building systems that can handle continuous data streams efficiently.

What's Next for Phishing Detection System

My next steps include: Enhanced Email Analysis through improving email content analysis using advanced NLP models, potentially incorporating transformers to better understand phishing patterns in language. User Alerts and Reporting: Building a user-facing alert system that notifies users when they encounter a potential phishing attempt with a simple reporting mechanism for suspicious emails or URLs. Model Improvements with Semi-Supervised Learning through exploring semi-supervised learning to improve detection in situations where labeled data is limited. Collaborations with Security Providers by partnering with cybersecurity firms to share data and improve threat intelligence, broadening the detection capabilities of our system.

Built With

• ai
• api
• gemini
• glove
• google
• matplotlib
• pandas
• scikit-learn
• tensorflow