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Web Hydra: AI-Powered Web Application Firewall

A 60-second introduction to our deep learning-driven WAF for modern threats

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Web Hydra
Intelligent Web Application Firewall Powered by Deep Learning
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Web Hydra
Intelligent Web Application Firewall Powered by Deep Learning
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The Cyber Threat Landscape
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Why Traditional WAFs Fail
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    2,200 cyberattacks per web app daily — one every 39 seconds
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    $4.45M average cost of a single data breach globally
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    277 days to identify and contain a breach traditionally
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    43% of breaches start with web app vulnerabilities
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The Core Problem
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Gaps in Today’s Firewalls
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    Static rule-based defense fails against zero-day attacks
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    High false positives block legitimate traffic
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    No automated mitigation leaves developers exposed
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    Web Hydra adapts with deep learning
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    Reduces false positives to 9.1%
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    Provides automated vulnerability guidance
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Our Solution
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Defense-in-Depth with Deep Learning
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    Signature detection via OWASP Core Rule Set for known CVEs
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    Zero-day anomalies flagged by deep learning autoencoders
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    Behavioral profiling catches slow-burn attack patterns
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    Threat intel integration for dynamic IoC verification
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System Architecture Overview
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Diagram of a 9-layer proxy with phases labeled: Filter, ML, Behavior, Intel
Diagram of a 9-layer proxy with phases labeled: Filter, ML, Behavior, Intel
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“
87.4% overall detection rate — matching enterprise WAF tools
Benchmark against open-appsec, F5, AWS, and Cloudflare
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Key Results and Benchmarks
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    98% test accuracy across 10 attack classes (CNN)
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    ~0ms latency on signature engine (Phase 1)
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    11 behavioral archetypes identified in sessions
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    Outperformed 7 of 12 commercial WAFs in detection
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What We Built
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Contributions and Achievements
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    Built a 9-layer defense-in-depth architecture in Docker
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    Deployed real-time autoencoders and CNNs in a FastAPI proxy
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    Implemented SHA-256 dynamic user tracking and OSINT integration
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    Achieved 90.9% true-negative rate, reducing false positives
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Web Hydra
Thank You. Questions?

References available upon request • Team: Abdelrahman, Amr, Taqi, Yousif, Ziad

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