Enterprise AI Analysis
Explainable Phishing Website Detection for Secure and Sustainable Cyber Infrastructure
This report details an innovative AI-driven approach to enhancing cybersecurity by detecting phishing websites with explainable machine learning, improving both accuracy and interpretability for robust digital infrastructure. Authored by Tanzila Kehkashan, Maha Abdelhaq, Ahmad Sami Al-Shamayleh, Nazish Huda, Imran Ashraf Yaseen, Abdelmuttlib Ibrahim Abdalla Ahmed, and Adnan Akhunzada.
Executive Impact & Strategic Value
Implementing explainable AI for phishing detection offers significant strategic advantages, reducing financial losses, enhancing operational security, and building trust in digital interactions.
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Phishing Detection Accuracy
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Reduction in False Positives
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Faster Incident Response
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Performance
Methodology
Features
Business Value
Enhanced Detection Performance
The proposed RF+SHAP model achieves state-of-the-art results in phishing detection.
97.0%
Overall Accuracy with SHAP-enhanced RF
| Model / Paper Name | Accuracy (%) | F1-Score (%) | Precision (%) | Recall (%) |
|---|---|---|---|---|
| Proposed RF + SHAP | 97.0 | 97.3 | 97.0 | 97.6 |
| RF (Baseline)66 | 96.25 | 96.2 | 97.6 | 98.3 |
| SVM67 | 94.2 | 94.8 | 93.5 | 96.1 |
| CNN+LSTM (IPDS)68 | 93.28 | 93.29 | 93.30 | 93.27 |
| DNN69 | 92.89 | 92.21 | 92.75 | 93.07 |
| LightGBM70 | 95.1 | 95.3 | 95.2 | 95.5 |
| DeepSeek R1 Distill Qwen 14B Q871 | 75 | 76 | 81 | 72 |
Innovative Detection Methodology
The research proposes an innovative explainable detection framework combining SHAP with supervised machine learning models, primarily based on URL features.
Enterprise Process Flow
Data preparation
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Data preprocessing
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SHAP Feature Selection
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5-fold cross validation
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Build the Model
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Performance Evaluation
Key Features & Interpretability
SHAP enhances predictive accuracy and model interpretability by prioritizing the most relevant features in phishing detection.
SHAP-driven Interpretability & Performance Boost
SHAP significantly improves model interpretability by highlighting key features, which in turn reduces misclassification rates and enhances predictive accuracy across models, strengthening the reliability of phishing detection systems.
Most Influential Features for RF
For the Random Forest model, 'HTTPS' and 'AnchorURL' are identified by SHAP as the most critical features driving phishing detection, demonstrating the model's reliance on secure protocol usage and URL structure.
Strategic Business Value
The proposed solution offers practical usability, being interpretable, cost-effective, and deployable in resource-limited environments.
Cost-Effective & Deployable Solution
The proposed SHAP-enhanced ML framework is designed for practical usability, offering an interpretable, cost-effective, and deployable solution suitable for real-world resource-limited cybersecurity environments.
Addressing Feature Significance Gaps
Problem: Traditional phishing detection methods often fail to adequately identify and leverage the significance of individual URL features, leading to suboptimal detection performance and a lack of interpretability.
Solution: This research addresses this gap by integrating Shapley Additive Explanations (SHAP) with URL-based machine learning models, enhancing feature selection, improving detection accuracy, and providing critical insights into feature contributions.
Outcome: The SHAP-enhanced approach achieves superior predictive performance while offering clear, human-interpretable explanations of feature importance, crucial for cybersecurity professionals.
Calculate Your Potential ROI
Estimate the financial and operational benefits of implementing explainable AI for phishing detection in your enterprise.
Estimated Annual Savings
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Employee Hours Reclaimed Annually
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Your AI Implementation Roadmap
A phased approach to integrating explainable phishing detection into your cybersecurity framework.
Phase 1: Discovery & Strategy
Assess current phishing detection capabilities, define enterprise-specific risks, and tailor an explainable AI strategy that aligns with your security objectives and resource constraints.
Phase 2: Data Integration & Model Training
Prepare and integrate diverse URL and network data, train and optimize SHAP-enhanced ML models, ensuring robust performance and interpretability for your environment.
Phase 3: Pilot Deployment & Testing
Deploy the explainable phishing detection system in a controlled pilot environment, rigorously test its accuracy, interpretability, and resource efficiency, and gather feedback for refinement.
Phase 4: Full-Scale Integration & Monitoring
Implement the system across your enterprise infrastructure (e.g., email gateways, browser plug-ins), establish continuous monitoring, and set up adaptive retraining mechanisms to counter evolving threats.
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