Idea
Phishing detection platform delivering real-time, highly accurate URL threat identification with minimal false positives.
Research Paper
Core Innovation
This paper introduces an ensemble model combining character-level CNN for sequential feature extraction with LightGBM using engineered lexical and domain features. This hybrid approach improves phishing URL detection accuracy and reduces false positives compared to individual models.
Why It Matters
Phishing remains a top cybersecurity threat causing significant financial and data losses. This system improves detection accuracy and reduces false alarms, enabling organizations to protect users and infrastructure more effectively. Its real-time capability supports scalable deployment across industries facing evolving phishing tactics.
Market Size (TAM)
$10–20B TAM for cybersecurity threat detection; $2–5B SAM from enterprises and financial institutions. Driven by rising phishing attacks and regulatory compliance demands.
Potential Customers & Pain Points
- Enterprises – Need to prevent phishing attacks and data breaches
- Cybersecurity firms – Require advanced detection tools
- Financial institutions – Need to protect customers from fraud
- Cloud service providers – Need scalable real-time threat detection
Business Model
Subscription-based SaaS offering API access and real-time phishing detection services to enterprises and cybersecurity vendors.
Competitive Landscape
- PhishLabs
- Cofense
- Proofpoint
- Microsoft Defender
- Google Safe Browsing
Implementation Challenges
- Rapid evolution of phishing tactics requiring continuous model updates
- Integration challenges with existing security infrastructure
- User trust and adoption of automated detection systems
Validation Strategy
- Pilot deployments with financial institutions and cybersecurity firms
- Benchmarking against existing phishing detection solutions
- Continuous model retraining with new phishing datasets
Research Paper Overview
Phishing Detection System: An Ensemble Approach Using Character-Level CNN and Feature Engineering
Summary
This paper presents an AI-based phishing detection system combining character-level CNN and LightGBM with engineered URL features. The ensemble model achieves over 99.8% accuracy and maintains low false positive rates, outperforming individual models. It is deployed via a FastAPI service for real-time phishing URL detection.