PhishGuardAI
Protect Yourself from Phishing Attacks
Our advanced AI-powered ensemble system helps you identify and avoid phishing websites with real-time analysis and cutting-edge machine learning technology.
Discover why and how we developed this AI solution
The Growing Threat of Phishing
Cybercriminals are becoming increasingly sophisticated, using advanced techniques to deceive users and steal sensitive information. The traditional security measures are no longer sufficient to combat these evolving threats.
3.4 Billion
Phishing emails sent daily
65%
Increase in phishing attacks (2023)
1 in 4
People fall victim to phishing
$4.91M
Average cost of a data breach
Why We Need a New Approach
Evolving Tactics: Phishers constantly adapt their methods to bypass traditional security filters
Human Error: Even security-aware users can fall victim to sophisticated phishing attempts
Scale of Attack: Millions of new phishing websites are created daily, making manual detection impossible
Financial Impact: Organizations lose billions annually due to successful phishing attacks
Our AI-Powered Solution
Harnessing the power of artificial intelligence and machine learning to create an intelligent defense system against sophisticated phishing attacks.
Why AI is the Answer
Traditional rule-based security systems cannot keep pace with the rapidly evolving landscape of cyber threats. AI offers unique advantages that make it the ideal solution for modern cybersecurity challenges.
Adaptive Learning: AI systems continuously learn from new threats and adapt their detection mechanisms
Pattern Recognition: Machine learning can identify subtle patterns that humans might miss
Scalability: AI can analyze millions of URLs simultaneously without performance degradation
Machine Learning
Intelligent models that detect phishing patterns by learning from large datasets
Real-time Analysis
Instant URL scanning and threat detection with millisecond response times
Ensemble Methods
Multiple AI models working together for superior accuracy and reliability
Precision Detection
High accuracy rates with minimal false positives for better user experience
Our Research Foundation
Extensive research and analysis form the backbone of our AI-powered phishing detection system. We've studied the latest developments in cybersecurity and machine learning to build a robust solution.
Literature Review
Comprehensive analysis of existing phishing detection methods and their limitations
30+ research papers analyzedDataset Analysis
Extensive study of phishing and legitimate website datasets for model training
100,000+ URLs analyzedAlgorithm Comparison
Systematic evaluation of different machine learning algorithms for phishing detection
15+ algorithms testedFeature Engineering
Research on optimal URL features for accurate phishing website identification
15+ features evaluatedKey Research Findings
Ensemble methods significantly outperform individual algorithms
URL structure analysis is more reliable than content-based features
Real-time processing requires optimized feature extraction
Weighted soft voting improves overall model accuracy by 12%
Research Methodology
Our research follows a systematic approach combining quantitative analysis of existing datasets with qualitative evaluation of current detection methods.
Publication Status
Research findings are being prepared for submission to international cybersecurity conferences and peer-reviewed journals.
Our Methodology
A systematic approach combining multiple machine learning algorithms in an ensemble framework for superior phishing detection accuracy and reliability.
Data Collection & Preprocessing
Gathered diverse datasets of phishing and legitimate URLs with comprehensive feature extraction
Ensemble Model Design
Implemented multiple machine learning algorithms with weighted soft voting mechanism
Model Training & Optimization
Systematic training and hyperparameter tuning for optimal performance
Testing & Validation
Comprehensive testing with real-world scenarios and performance benchmarking
Detection Process Flow
URL Input
User submits URL
Feature Extraction
30 features extracted
Ensemble Analysis
4 algorithms process
Result
Safe or Phishing
Model Performance & Outcomes
Our ensemble machine learning model achieves exceptional performance metrics, demonstrating superior accuracy and reliability in real-world phishing detection scenarios.
89.2%
Overall Accuracy
92.1%
Precision
85.7%
Recall
88.8%
F1-Score
Key Model Attributes
Real-time Processing
Average response time under 500ms for instant threat detection
High Accuracy
89.2% accuracy rate with minimal false positives
Scalable Architecture
Can handle thousands of concurrent URL analysis requests
Adaptive Learning
Continuously improves with new phishing patterns and data
Feature Importance Analysis
Our analysis reveals the most critical URL features that contribute to accurate phishing detection. These insights help improve model interpretability and performance.
Training Dataset
100,000+ URLs analyzed
14 features extracted
4 algorithms ensemble
PhishGuardAI Interface
Experience our user-friendly interface designed for seamless phishing detection. Simply enter a URL and get instant results with detailed threat analysis.
Desktop Optimized
Full-featured interface with comprehensive analysis dashboard
Tablet Friendly
Responsive design that adapts to tablet screens seamlessly
Mobile Ready
Quick URL checking on-the-go with mobile-optimized interface
Technical Specifications
Built with modern web technologies and best practices to ensure optimal performance, security, and user experience across all platforms.
Frontend Technologies
HTML5
Modern semantic markup
CSS3
Advanced styling and animations
JavaScript - React Framework (NextJs)
Interactive user experience
TailwindCSS & Framer Motion
Responsive framework with Animations
Backend & ML Stack
Python
Core ML implementation
Scikit-learn
Machine learning algorithms
Pandas
Data processing and analysis
FastAPI
RESTful API development
Performance & Compatibility Features
Fast Loading Optimization
Optimized code and asset compression for lightning-fast performance
Cross-browser Compatibility
Consistent experience across all modern web browsers
Mobile-first Approach
Designed for mobile devices with progressive enhancement
Accessibility Standards
WCAG 2.1 compliant for inclusive user experience
Performance Benchmarks
Response Time
Average API response time for URL analysis
Uptime
System availability and reliability
Concurrent Users
Supported simultaneous analysis requests
Meet Our Team
Group 46 from the Faculty of Technology at University of Sri Jayewardenepura, dedicated to advancing cybersecurity through innovative AI solutions.
AI-BASED ENSEMBLE METHODS FOR DETECTING PHISHING ATTACKS IN REAL-TIME
A Final Year Research Project by Group 46
Research Team Members
F.A.M. Rimsan
ICT/20/918
Network Technology
ICT Department
S.I.M. Rismi
ICT/20/919
Network Technology
ICT Department
Mahendra Pennikin
ICT/20/964
Software Technology
ICT Department
Academic Supervisors
Dr. P.L.M. Prabhani
Academic Supervisor
Department of Information Communication Technology
Faculty of Technology
University of Sri Jayewardenepura
Mrs. Sankani Heenkenda
Academic Co-Supervisor
Department of Information Communication Technology
Faculty of Technology
University of Sri Jayewardenepura
Get in Touch
Interested in our research or have questions about PhishGuardAI? We'd love to hear from you.