fix startup order

README.md

@@ -7,73 +7,85 @@ sdk: docker
 app_port: 7860
 pinned: false
 ---
-# AI Cyber Deception OpenEnv
+
+# 🛡️ AI Cyber Deception OpenEnv
 
 ## Overview
 
-This project implements a real-world AI Cyber Deception environment where an AI agent learns to detect and mitigate cyber attacks such as brute force, port scanning, and credential stuffing.
+AI Cyber Deception OpenEnv is a real-world cybersecurity simulation environment where an AI agent learns to detect, deceive, and mitigate cyber attacks.
+
+This environment simulates production-like cybersecurity defense scenarios including brute force attacks, port scanning, and credential stuffing.
+
+The environment follows the **OpenEnv specification** and supports:
 
-The environment follows the OpenEnv specification and supports step(), reset(), and state() APIs.
+- `reset()`
+- `step()`
+- `state()`
 
 ---
 
-## Real-world Task
+## 🎯 Real-World Task
 
 Simulate cybersecurity defense in a production-like environment:
 
 - Detect brute force attacks
 - Detect port scanning
 - Deploy deception mechanisms
-- Block malicious IPs
+- Block malicious attackers
 
 ---
 
-## Action Space
+## ⚙️ Action Space
 
-The agent can perform:
+The AI agent can perform the following actions:
 
-- detect_attack
-- deploy_honeypot
-- fake_database
-- block_ip
+- `detect_attack`
+- `deploy_honeypot`
+- `fake_database`
+- `block_ip`
 
 ---
 
-## Observation Space
+## 👁️ Observation Space
 
-Environment returns:
+Environment returns structured observation:
 
-- failed_logins
-- port_scans
-- suspicious_ips
-- request logs
+- `failed_logins`
+- `port_scans`
+- `suspicious_ips`
+- `request_logs`
 
 ---
 
-## Tasks
+## 🧠 Tasks
 
 ### Easy Task
 Detect brute force attack
 
 ### Medium Task
-Deploy honeypot after detecting attack
+Detect attack and deploy honeypot
 
 ### Hard Task
-Block malicious attacker
+Detect, deceive, and block attacker
 
 ---
 
-## Reward Function
+## 🏆 Reward Function
 
 | Action | Reward |
 |--------|--------|
-| detect_attack | 0.4 |
-| deploy_honeypot | 0.2 |
-| block_ip | 0.3 |
+| detect_attack | 0.20 |
+| deploy_honeypot | 0.30 |
+| fake_database | 0.20 |
+| block_ip | 0.50 |
+
+Reward range normalized between **0.0 – 1.0**
 
 ---
 
-## APIs
+## 🌐 API Endpoints
+
+Available endpoints:
 
 - `/reset`
 - `/step`
@@ -81,35 +93,127 @@ Block malicious attacker
 - `/logs`
 - `/status`
 
+Example:
+
+
+POST /reset
+POST /step
+GET /state
+
+
 ---
 
-## Setup
+## 🚀 Run Locally
 
-### Run locally
+Install dependencies:
 
 ```bash
 pip install -r requirements.txt
+
+Run inference:
+
 python inference.py
 
-Docker
+🐳 Docker
+
+Build:
+
 docker build -t ai-deception .
-docker run ai-deception
 
-Hugging Face Deployment
+Run:
+
+docker run -p 7860:7860 ai-deception
+
+🤗 Hugging Face Deployment
+Live Space:
+
 https://bytecore1-ai-deception-openenv.hf.space/
+
+Endpoints:
+
+https://bytecore1-ai-deception-openenv.hf.space/reset
 https://bytecore1-ai-deception-openenv.hf.space/state
 https://bytecore1-ai-deception-openenv.hf.space/status
 https://bytecore1-ai-deception-openenv.hf.space/logs
 
-Baseline Results
-
-Example:
-
-[START]
-[STEP]
-[STEP]
-[STEP]
-[END]
-Architecture
-
-Attacker → Fake Server → AI Agent → Defense Actions → Reward
+📊 Baseline Results
+Example run:
+
+[START] task=ai-deception env=cyber-security model=Qwen
+[STEP] step=1 action=detect_attack reward=0.20 done=false error=null
+[STEP] step=2 action=deploy_honeypot reward=0.30 done=false error=null
+[STEP] step=3 action=block_ip reward=0.50 done=true error=null
+[END] success=true steps=3 score=1.00 rewards=0.20,0.30,0.50
+
+
+🏗️ Architecture
+Attacker
+   ↓
+Fake Server
+   ↓
+AI Agent (Inference)
+   ↓
+Defense Actions
+   ↓
+Reward
+
+📦 Project Structure
+
+ai-deception-openenv/
+│
+├── env/
+│   ├── __init__.py
+│   ├── attacker.py
+│   ├── deception.py
+│   ├── env.py
+│   ├── fake_server.py
+│   ├── test_env.py
+│   └── test_server.py
+│
+├── tasks/
+│   ├── __init__.py
+│   ├── easy.py
+│   ├── medium.py
+│   ├── hard.py
+│   └── test_tasks.py
+│
+├── inference.py
+├── app.py
+├── models.py
+├── openenv.yaml
+├── Dockerfile
+├── requirements.txt
+├── README.md
+├── .gitignore
+└── .gitattributes
+
+
+✅ OpenEnv Compliance
+reset() implemented
+step() implemented
+state() implemented
+Docker support
+Structured logs
+Multiple tasks
+Reward normalization
+
+👨‍💻 Use Case
+This environment can be used for:
+
+Cybersecurity research
+Reinforcement learning
+AI defense strategy training
+Red team vs blue team simulations
+
+🛡️ AI Cyber Deception
+This project demonstrates how AI can:
+
+Detect attackers
+Deploy deception
+Block malicious actors
+Learn defensive strategies
+
+
+License
+
+MIT License