Enterprise AI Analysis
FedPhishLLM: A privacy-preserving and explainable phishing detection mechanism using federated learning and LLMs
This study introduces FedPhishLLM, a novel framework integrating Federated Learning (FL) with fine-tuned multimodal Large Language Models (LLMs) for privacy-preserving and explainable phishing detection. It addresses critical limitations of traditional centralized methods by enabling decentralized model training, preserving user privacy, and enhancing detection accuracy and interpretability across diverse linguistic environments.
Executive Impact at a Glance
FedPhishLLM delivers robust, privacy-conscious phishing defense, setting new benchmarks in cybersecurity. Its explainable AI fosters user trust, while federated learning ensures adaptability and scalability for enterprise deployments.
Detection Accuracy
Recall Rate
Privacy Preservation
Real-time Inference
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 Metrics
Methodology Flow
Model Comparison
Explainability Case Study
Feature Contributions
Adversarial Resilience
Enhanced Detection Performance
After fine-tuning, FedPhishLLM achieves 95% accuracy, 95% precision, 96% recall, and an F1-score of 0.96, significantly outperforming pre-trained models and most baselines. This demonstrates the critical role of custom adaptation for specialized security tasks.
Achieved F1-score after fine-tuning
Enterprise Process Flow
FedPhishLLM Workflow
Client Data Preparation (URL, HTML, Screenshots)
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Local LoRA Fine-tuning on VLM
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Client Transmits Adapted Parameters to Server
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Server Aggregates Global LoRA Adapter
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Redistribute Global Adapter to Clients
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Real-time Phishing Detection & Explanation
Comparative Model Performance (FL Setting)
FedPhishLLM consistently achieves superior performance in Federated Learning (FL) settings compared to traditional ML/DL models and even other VLMs, highlighting its robustness in decentralized environments.
| Approach | Accuracy (FL) | Precision (FL) | Recall (FL) | F1 (FL) |
|---|---|---|---|---|
| MLP | 0.78 | 0.79 | 0.79 | 0.78 |
| CNN | 0.53 | 0.27 | 0.50 | 0.35 |
| NN+ResNet-18 | 0.84 | 0.84 | 0.84 | 0.84 |
| ViT-base-16 | 0.53 | 0.27 | 0.50 | 0.35 |
| Qwen2-VL-2B (Ours) | 0.95 | 0.95 | 0.96 | 0.96 |
Explainable Phishing Warnings
Case Study: PayPal Credential Theft
When presented with a phishing webpage mimicking PayPal, FedPhishLLM's explanation module not only blocks access but provides a detailed warning. It identifies the brand as PayPal and the intention as "to steal credentials, such as usernames, passwords, PINs, and authentication credentials." This actionable feedback significantly enhances user understanding and trust, preventing potential financial losses.
User study results show a clear shift in perceived legitimacy towards "phishing" after explanations are provided, increasing user confidence and informed decision-making.
Impact of Multimodal Features
A feature ablation study reveals that all four proposed feature categories (HTML-related, URL-based, domain age, and screenshot-based indicators) contribute positively to FedPhishLLM's detection capability. The largest performance drop occurred when HTML-related features were excluded, highlighting their pivotal role.
F1-score reduction without HTML-related features (from 0.9515 to 0.9112)
Robustness Against Adversarial Attacks
FedPhishLLM demonstrates strong resilience against various adversarial and evasive attacks, maintaining accuracy above 0.99 and perfect F1-scores (1.000) in several complex scenarios. Its client-based architecture naturally overcomes cloaking techniques that defeat traditional server-side detection systems.
| Adversarial Scenario | Accuracy | Recall | F1 |
|---|---|---|---|
| Adversarial Content with Legitimate URLs | 0.99 | 0.99 | 0.995 |
| Adversarial Visuals with Phishing URLs | 1.00 | 1.00 | 1.000 |
| Adversarial Content + Visuals with Phishing URLs | 1.00 | 1.00 | 1.000 |
Calculate Your Potential ROI with FedPhishLLM
Estimate the financial and operational benefits of implementing an AI-powered phishing detection system in your organization.
Estimated Annual Savings
$0
Total Hours Reclaimed Annually
0
Your FedPhishLLM Implementation Roadmap
A phased approach to integrating advanced phishing detection, ensuring seamless deployment and maximum security benefits for your organization.
Phase 1: Discovery & Strategy
Initial consultation to assess your current cybersecurity posture, identify specific phishing threat vectors, and define FedPhishLLM deployment objectives tailored to your enterprise environment.
Phase 2: Decentralized Setup & Data Integration
Establish federated learning infrastructure on client devices, integrate multimodal data sources (URLs, HTML, screenshots), and configure secure data handling protocols for privacy-preserving training.
Phase 3: Model Fine-tuning & Customization
Perform local LoRA fine-tuning with tailored prompts on client-specific data, optimizing the VLM for regional linguistic patterns and emerging phishing tactics unique to your user base.
Phase 4: Global Aggregation & Deployment
Aggregate local model updates on the central server, distribute global LoRA adapters, and deploy the fine-tuned FedPhishLLM for real-time, explainable phishing detection across all client endpoints.
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