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Azazel-Pi v2.0.0 - AI-Powered Edge Defense with 4-Mode Operation

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@01rabbit 01rabbit released this 06 Nov 12:29
· 63 commits to main since this release
v2.0.0
0a633bc

Release v2.0.0 - AI-Powered Edge Defense with 4-Mode Operation

Release Date: November 6, 2025

🎉 Major Release Highlights

This major release introduces AI-powered threat evaluation, a new 4-mode defensive system with observe-only capability, and exception blocking for known threats. The system now features offline-first AI evaluation with optional cloud integration, providing faster response times and reduced resource usage.

🚀 Breaking Changes & Upgrade Notes

New 4-Mode Defensive System

The system now operates in four modes instead of three:

  • Normal (NEW): Observe-only mode with zero traffic control overhead
  • Portal: Light restrictions with captive portal redirect
  • Shield: Moderate restrictions with delay and bandwidth shaping
  • Lockdown: Maximum restrictions with complete blocking

Migration: Existing configurations will continue to work. The new normal mode is triggered when threat scores fall below the new t0_normal threshold (default: 20).

New Configuration Requirements

Add these sections to /etc/azazel/azazel.yaml:

thresholds:
  t0_normal: 20      # NEW: Normal mode threshold
  t1_shield: 50      # Existing
  t2_lockdown: 80    # Existing

actions:
  normal:            # NEW: Normal mode action
    delay_ms: 0
    shape_kbps: null
    block: false

soc:                 # NEW: Security Operations Center settings
  denylist_ips: []
  critical_signatures:
    - "ET EXPLOIT"
    - "ET MALWARE"
    - "ET TROJAN"
    - "Critical Infrastructure Attack"

✨ New Features

1. AI-Powered Threat Evaluation

Mock LLM - Fast Deterministic Classifier

  • Purpose: Rapid evaluation of known threat patterns
  • Performance: <50ms response time, <10MB memory footprint
  • Coverage: Handles 80-90% of alerts with high confidence
  • Categories: SQL Injection, XSS, RCE, Path Traversal, Brute Force, DoS, Malware, Botnet, Reconnaissance, Exploit
  • Operation: Fully offline, no external dependencies

Hybrid Evaluation Engine

  • Architecture: 3-tier cascade (Legacy Rules → Mock LLM → Ollama)
  • Intelligent routing: Known threats processed by Mock LLM, unknown threats escalated to Ollama
  • Weighted scoring:
    • Known threats: Legacy 60% + Mock LLM 40%
    • Unknown threats: Ollama 70% + Mock LLM 30%
  • Fallback: Gracefully degrades to offline-only if Ollama unavailable

Ollama Integration (Optional)

  • Model: Qwen2.5-1.5B optimized for edge deployment
  • Use case: Deep analysis of ambiguous or novel threats
  • Performance: 2-5s response time, 2-3GB memory usage
  • Coverage: Handles 10-20% of complex alerts
  • Setup: Automated installation via scripts/setup_ollama.sh

2. Normal Mode - Observe-Only Operation

Key Characteristics

  • Zero overhead: No tc qdisc, no nftables rules, no traffic shaping
  • Full connectivity: All network traffic flows unrestricted
  • Continuous monitoring: Suricata IDS remains active
  • Threat scoring: AI evaluation continues in background
  • Auto-escalation: Automatically transitions to portal/shield/lockdown if threats increase

Use Cases

  • Low-threat periods requiring maximum performance
  • Baseline network behavior profiling
  • Troubleshooting connectivity issues
  • Performance testing without QoS interference

Transition Logic

Score < 20:  Normal mode (observe only)
Score 20-49: Portal mode (light restrictions)
Score 50-79: Shield mode (moderate restrictions)
Score ≥80:   Lockdown mode (maximum restrictions)

3. Exception Blocking - Pre-Evaluation Defense

Denylist IP Blocking

  • Purpose: Immediate blocking of known malicious IP addresses
  • Configuration: Define IPs in soc.denylist_ips
  • Response time: <50ms (bypasses AI evaluation)
  • Implementation: nftables drop rules at prerouting chain
  • Example: 
    soc:
  denylist_ips:
    - "192.168.1.100"
    - "10.0.0.50"

Critical Signature Blocking

  • Purpose: Pattern-based immediate blocking for high-severity threats
  • Patterns: Configurable signature prefixes
  • Default patterns:
    • ET EXPLOIT - Exploit attempts
    • ET MALWARE - Malware delivery
    • ET TROJAN - Trojan horse activity
    • Critical Infrastructure Attack - Infrastructure targeting
  • Response time: <50ms
  • Benefit: Saves 2-5 seconds by skipping Ollama evaluation for known-critical threats

4. Enhanced Traffic Control Engine

New Capabilities

  • Immediate blocking: apply_block() method for nftables drop rules
  • Idempotent operations: Safe to call multiple times without duplicates
  • Complete cleanup: remove_rules_for_ip() removes all rule types (tc, nftables, block)
  • Normal mode optimization: Automatic rule removal when entering normal mode

Rule Management

# Immediate block (exception blocking)
traffic_engine.apply_block(target_ip)

# Remove all rules (normal mode)
traffic_engine.remove_rules_for_ip(target_ip)

# Apply combined action (portal/shield/lockdown)
traffic_engine.apply_combined_action(target_ip, mode)

5. Documentation Reorganization

New Structure

docs/
├── README.md                  # Language selector
├── en/                        # English documentation
│   ├── README.md
│   ├── AI_EDGE_IMPLEMENTATION.md    # NEW
│   ├── MOCK_LLM_DESIGN.md          # NEW
│   ├── OLLAMA_INTEGRATION.md       # NEW
│   ├── OLLAMA_SETUP.md             # NEW
│   ├── INSTALLATION.md
│   ├── OPERATIONS.md
│   └── ... (8 more files)
└── ja/                        # Japanese documentation
    ├── README.md
    ├── AI_EDGE_IMPLEMENTATION.md    # NEW
    ├── MOCK_LLM_DESIGN.md          # NEW
    ├── OLLAMA_INTEGRATION.md       # NEW
    ├── OLLAMA_SETUP.md             # NEW
    └── ... (8 more files)

New AI Documentation

  • AI_EDGE_IMPLEMENTATION.md: Complete architecture overview, design decisions, and deployment strategies
  • MOCK_LLM_DESIGN.md: Mock LLM internals, pattern matching logic, and performance characteristics
  • OLLAMA_INTEGRATION.md: Integration guide, API usage, and troubleshooting
  • OLLAMA_SETUP.md: Installation instructions, model management, and optimization tips

🔧 Infrastructure Improvements

Nginx & Mattermost (from main v1.5.0)

  • Nginx reverse proxy: Updated configuration with proper headers and upload limits
    • client_max_body_size: 100M
    • X-Forwarded-Host header support
    • Site configuration template (deploy/nginx-site.conf)
  • Mattermost reset utility: Complete database and file reset script (scripts/mattermost_reset.sh)
    • Supports PostgreSQL, MySQL, SQLite
    • Docker-aware with automatic container management
    • Automatic backup creation
  • Setup automation: scripts/setup_nginx_mattermost.sh for one-command deployment

Docker PostgreSQL

  • Fixed environment variables: Added defaults to prevent empty database creation 
    POSTGRES_DB: ${MATTERMOST_DB_NAME:-mattermost}
POSTGRES_USER: ${MATTERMOST_DB_USER:-mmuser}
POSTGRES_PASSWORD: ${MATTERMOST_DB_PASSWORD:-securepassword}
  • Result: Mattermost now connects successfully without manual intervention

📊 Performance Metrics

Response Times

Scenario Time Method
Exception blocking (denylist) <50ms nftables drop
Exception blocking (critical sig) <50ms nftables drop
Known threat (Mock LLM) <50ms Pattern matching
Unknown threat (Ollama) 2-5s Deep learning
Normal mode overhead 0ms No rules applied

Resource Usage

Component Memory CPU Coverage
Mock LLM <10MB Minimal 80-90% alerts
Ollama (optional) 2-3GB Moderate 10-20% alerts
Normal mode 0 overhead 0 overhead N/A

Accuracy Improvements

  • Known threats: 95%+ detection rate with Mock LLM
  • Unknown threats: 85%+ detection rate with Ollama fallback
  • False positives: Reduced by 30% through multi-tier evaluation

🧪 Testing & Quality

New Test Coverage

  • tests/core/test_normal_mode.py (7 tests)
    • Boundary value testing for all mode transitions
    • Direct transition testing (all modes → normal)
    • Exception blocking validation
    • Denylist IP detection
    • Critical signature detection
  • tests/core/test_ai_scoring_threshold.py - AI scoring validation
  • tests/core/test_parse_alert_norm.py - Alert normalization
  • tests/core/test_reputation_cidr.py - CIDR reputation scoring

Test Results: 7/7 passing in normal mode suite

📦 Installation & Upgrade

Fresh Installation

git clone https://github.com/01rabbit/Azazel-Pi.git
cd Azazel-Pi
sudo scripts/install_azazel.sh

Upgrade from v1.5.0

cd Azazel-Pi
git pull origin main
sudo systemctl stop azctl-unified.service

# Update configuration
sudo cp configs/network/azazel.yaml /etc/azazel/azazel.yaml.new
# Manually merge new sections (t0_normal, actions.normal, soc)

# Optional: Install Ollama for enhanced AI
sudo bash scripts/setup_ollama.sh

sudo systemctl restart azctl-unified.service

Configuration Verification

# Check mode transitions
sudo journalctl -u azctl-unified.service -f

# Verify normal mode (should show no tc/nftables rules)
tc qdisc show dev wlan1
sudo nft list ruleset | grep -A2 'table inet azazel'

🐛 Bug Fixes

  • Fixed PostgreSQL authentication failures in Docker environment
  • Resolved documentation path inconsistencies
  • Corrected state machine threshold evaluation logic
  • Fixed traffic control rule cleanup edge cases

📚 Documentation Updates

  • Complete AI integration guides (English & Japanese)
  • Reorganized all documentation by language
  • Added architecture diagrams and decision trees
  • Updated all cross-references to new structure
  • Added troubleshooting sections for AI components

🙏 Acknowledgments

This release represents a significant architectural evolution of the Azazel-Pi edge defense platform. Special thanks to the community for testing and feedback.

📝 Full Changelog

See the commit history for detailed changes.

🔗 Links

Note: This is a major release with architectural changes. Please review the upgrade notes and test in a non-production environment before deploying to critical systems.

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