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ZenFlood is an advanced, multi-protocol slow HTTP research tool for red team, adversarial AI, and defensive simulation.

• HTTP/1.1 (classic Slowloris) and HTTP/2 (multiplexed slow attacks)
• Randomized, obfuscated, jittered headers and payloads
• Supports both slow GET and slow POST (chunked)
• Captures server responses for behavioral and ML analysis
• Designed for research, not crime — authorized use only

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Features

• HTTP/1.1 & HTTP/2 support: Modern coverage, cloud/CDN-ready
• Header & Payload Randomization: Mimics advanced bots, defeats naive heuristics
• Timing Jitter: Simulates organic slow clients, evades static rules
• Response Logging: Enables ML, detection, and adversarial signal analysis
• Easy to Extend: Modular code for rapid experimentation

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Installation

pip install h2 pysocks

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Usage

Classic Slowloris (HTTP/1.1, HTTPS):

python3 zenflood.py example.com --https

Slowloris with HTTP/2 (modern, multiplexed):

python3 zenflood.py example.com --https --http2

Slow POST / Chunked transfer:

python3 zenflood.py example.com --https --mode post

Advanced Jitter (adversarial/realistic traffic):

python3 zenflood.py example.com --https --header-jitter-min 0.5 --header-jitter-max 2 --payload-jitter-min 4 --payload-jitter-max 10

Full options:

python3 zenflood.py -h

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Example Output

[+] Active sockets: 100 | Recent log: [b'HTTP/1.1 503 Service Unavailable\r\n...']

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Research and Defensive Use

ZenFlood is built for:

• Adversarial red team and XDR/SIEM/AI dataset generation
• Real-world simulation of slow HTTP/1.1 and HTTP/2 DoS for blue teams
• Testing rate-limiting, WAF, and CDN protections in hybrid/cloud environments

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Ethical Notice

Only use ZenFlood with explicit, written authorization on systems you own or are approved to test. This tool is for research and defense — not for unauthorized activity, crime, or vandalism.

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Attribution

Original by ZenRat-47 Modernized and maintained by Kai Aizen 2025 — For the security community and responsible adversarial research

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License

MIT License

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Roadmap

• HTTP/3/QUIC support (planned)
• ML integration for automated label generation
• AsyncIO-based high-scale mode

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Open to contributions, feedback, and professional collaboration.

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**You can copy-paste this directly for your repo.**  
Let me know if you want additional badges, FAQ, or ML integration examples!