Advanced AI-powered cybersecurity platform that combines machine learning, real-time threat detection, and intelligent deception to provide enterprise-grade protection against modern cyber threats.
ShadowWall AI is a cutting-edge, enterprise-grade cybersecurity platform that employs artificial intelligence, machine learning, and advanced deception techniques to provide comprehensive protection against sophisticated cyber threats. Designed for security professionals, SOC teams, and organizations requiring proactive threat defense.
+ π§ AI-powered threat detection with 97%+ accuracy
+ π Adaptive deception strategies that evolve with attackers
+ π― Dynamic honeypots with intelligent service emulation
+ π Real-time threat visualization and enterprise dashboard
+ π¬ Advanced malware analysis sandbox
+ β‘ Sub-second threat detection and response
+ π Multi-cloud and hybrid deployment ready
+ π‘οΈ Enterprise-grade security and compliance- Advanced ML Models: Multi-layered machine learning with Random Forest, XGBoost, and Deep Learning
- Real-time Analysis: Live network traffic analysis with <100ms threat identification
- Behavioral Analytics: User and entity behavior analysis for insider threat detection
- Predictive Intelligence: Proactive threat prediction using historical patterns
- Zero-day Protection: ML-based detection of unknown threats and attack vectors
- Deep Packet Inspection: Real-time analysis across multiple network interfaces
- Traffic Pattern Analysis: Advanced analytics for suspicious network behavior
- Network Topology Mapping: Automated discovery and asset inventory
- Protocol Analysis: Support for TCP, UDP, ICMP, HTTP/HTTPS, DNS, and custom protocols
- Bandwidth Monitoring: Real-time bandwidth utilization and anomaly detection
- Multi-Service Honeypots: SSH, HTTP/HTTPS, FTP, SMTP, Database, and IoT honeypots
- Dynamic Configuration: Adaptive honeypot profiles based on threat intelligence
- Attack Simulation: Realistic service emulation to capture attacker techniques
- Evidence Collection: Comprehensive forensic logging and malware capture
- Threat Attribution: Advanced analysis of attacker methods and origins
- Multiple Feed Sources: Commercial, open-source, and government threat feeds
- STIX/TAXII Support: Industry-standard threat intelligence formats
- IOC Processing: Automated indicators of compromise correlation
- Threat Actor Profiling: Advanced attribution and campaign tracking
- Custom Intelligence: Organization-specific threat intelligence integration
- Real-time Visualization: Interactive threat monitoring with live updates
- Advanced Analytics: Comprehensive security metrics and trend analysis
- Custom Dashboards: Role-based views for different security personas
- Automated Reporting: Compliance and executive reporting capabilities
- Mobile Responsive: Full functionality across desktop and mobile devices
- Dynamic Analysis: Safe execution environment for malware investigation
- Behavioral Monitoring: System call, file, and network activity analysis
- Memory Forensics: Advanced memory dump analysis and artifact extraction
- YARA Integration: Custom and community YARA rules for signature-based detection
- Threat Hunting: Advanced search and investigation capabilities
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β ShadowWall AI Platform β
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β π Presentation Layer β
β βββ Next-Gen Dashboard (FastAPI + React) β
β βββ REST APIs & GraphQL β
β βββ WebSocket Real-time Updates β
β βββ Mobile-Responsive Interface β
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β π€ AI/ML Intelligence Engine β
β βββ Threat Detection (Random Forest, XGBoost, LSTM) β
β βββ Anomaly Detection (Isolation Forest, Autoencoders) β
β βββ Behavioral Analysis (Deep Learning, NLP) β
β βββ Predictive Analytics (Time Series, Neural Networks) β
β βββ Threat Attribution (Graph Neural Networks) β
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β π‘οΈ Security & Monitoring Components β
β βββ Network Monitor (Scapy, DPDK, Raw Sockets) β
β βββ Honeypot Manager (Multi-Protocol Support) β
β βββ Deception Engine (Dynamic Configuration) β
β βββ Threat Intelligence (STIX/TAXII, MISP) β
β βββ Incident Response (SOAR Integration) β
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β π¬ Analysis & Forensics β
β βββ Malware Sandbox (Containerized Execution) β
β βββ Memory Forensics (Volatility, Rekall) β
β βββ Network Forensics (Wireshark, Zeek) β
β βββ Digital Evidence (Chain of Custody) β
β βββ Threat Hunting (ElasticSearch, Splunk) β
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β πΎ Data & Storage Layer β
β βββ Time-Series DB (InfluxDB, TimescaleDB) β
β βββ Search Engine (Elasticsearch, Solr) β
β βββ Cache Layer (Redis Cluster) β
β βββ Object Storage (MinIO, S3) β
β βββ Relational DB (PostgreSQL, SQLite) β
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β π§ Infrastructure & DevOps β
β βββ Container Orchestration (Kubernetes, Docker Swarm) β
β βββ Service Mesh (Istio, Linkerd) β
β βββ Monitoring (Prometheus, Grafana) β
β βββ Logging (ELK Stack, Fluentd) β
β βββ CI/CD (GitLab CI, GitHub Actions) β
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- OS: Linux (Ubuntu 20.04+), macOS (10.15+), Windows (with WSL2)
- Python: 3.8 or higher
- RAM: 4GB minimum (8GB+ recommended)
- Storage: 10GB free space (50GB+ for production)
- Network: Interface access for packet capture
# Clone repository
git clone https://github.com/yashab-cyber/shadow-wall.git
cd shadow-wall
# Deploy with Docker Compose
docker-compose up -d
# Access dashboard
open http://localhost:8081# 1. Clone and setup
git clone https://github.com/yashab-cyber/shadow-wall.git
cd shadow-wall
# 2. Run automated deployment
chmod +x deploy.sh
./deploy.sh
# 3. Configure system
cp config/config.example.yaml config/config.yaml
# Edit config.yaml with your settings
# 4. Set environment variables
export SHADOWWALL_SECRET_KEY="$(openssl rand -hex 32)"
export SHADOWWALL_DB_PATH="data/shadowwall.db"
# 5. Start the platform
python run_integrated.py
# 6. Access dashboard
open http://localhost:8081# Apply Kubernetes manifests
kubectl apply -f k8s/
# Port forward to access dashboard
kubectl port-forward svc/shadowwall-dashboard 8081:8081| Component | Minimum | Recommended | Enterprise |
| CPU | 2 cores, 2.0 GHz | 4 cores, 2.5 GHz | 8+ cores, 3.0+ GHz |
| RAM | 4 GB | 8 GB | 16+ GB |
| Storage | 10 GB | 50 GB SSD | 200+ GB NVMe |
| Network | 100 Mbps | 1 Gbps | 10+ Gbps |
| GPU | Not required | CUDA-compatible | Multiple GPUs |
# Network Monitoring
network:
interfaces: ['eth0', 'wlan0']
capture_filter: "not host 127.0.0.1"
packet_buffer_size: 65536
analysis_threads: 4
# Machine Learning
ml:
models_path: "models/"
retrain_interval: 3600
threat_threshold: 0.75
anomaly_threshold: 0.85
feature_update_interval: 300
# Honeypots
honeypots:
ssh:
enabled: true
port: 2200
banner: "OpenSSH_8.9"
http:
enabled: true
port: 8000
server_header: "Apache/2.4.41"
ftp:
enabled: true
port: 2100
banner: "vsftpd 3.0.3"
# Dashboard
dashboard:
host: "0.0.0.0"
port: 8081
debug: false
ssl_enabled: true
jwt_secret: "${SHADOWWALL_SECRET_KEY}"
session_timeout: 3600
# Threat Intelligence
threat_intel:
feeds:
- name: "internal"
type: "file"
path: "data/threat_feeds/internal.json"
- name: "misp"
type: "http"
url: "${MISP_URL}/attributes/restSearch"
api_key: "${MISP_API_KEY}"
update_interval: 900
# Database
database:
url: "sqlite:///data/shadowwall.db"
pool_size: 10
max_overflow: 20
echo: false
# Logging
logging:
level: "INFO"
format: "structured"
output: "file"
rotation: "daily"
retention: "30d"GET /api/v3/threats/advanced?limit=50&severity=high
POST /api/v3/threats/analyze
PUT /api/v3/threats/{threat_id}/status
DELETE /api/v3/threats/{threat_id}
GET /api/v3/threats/stats/dashboardGET /api/v3/network/connections/active
GET /api/v3/network/traffic/realtime
GET /api/v3/network/topology/discover
POST /api/v3/network/capture/start
GET /api/v3/network/bandwidth/utilizationGET /api/v3/honeypots/status/all
POST /api/v3/honeypots/deploy
PUT /api/v3/honeypots/{honeypot_id}/config
GET /api/v3/honeypots/interactions/recent
DELETE /api/v3/honeypots/{honeypot_id}GET /api/v3/ml/models/performance
POST /api/v3/ml/models/retrain
GET /api/v3/ml/predictions/recent
POST /api/v3/ml/models/evaluate
GET /api/v3/ml/features/importance// Real-time threat alerts
const threatSocket = new WebSocket('ws://localhost:8081/ws/v3/threats');
// Live network monitoring
const networkSocket = new WebSocket('ws://localhost:8081/ws/v3/network');
// Honeypot interaction feed
const honeypotSocket = new WebSocket('ws://localhost:8081/ws/v3/honeypots');
// System health monitoring
const healthSocket = new WebSocket('ws://localhost:8081/ws/v3/system_health');- π Multi-Factor Authentication: TOTP, SMS, Email, Hardware tokens
- π₯ Role-Based Access Control: Granular permissions and user management
- π API Security: JWT tokens, OAuth2, rate limiting, API keys
- π Audit Logging: Comprehensive security event logging and SIEM integration
- π Data Encryption: TLS 1.3, AES-256, end-to-end encryption
- β Compliance: SOC 2, ISO 27001, NIST, GDPR compliance ready
- π― APT Detection: Advanced Persistent Threat identification and tracking
- π‘οΈ Zero-day Protection: ML-based detection of unknown attack vectors
- π€ Insider Threat Detection: Behavioral analysis for internal threats
- π IoT Security: Specialized protection for IoT and edge devices
- βοΈ Cloud Security: Multi-cloud environment monitoring and protection
| Metric | Performance | Enterprise Scale |
| Packet Processing Rate | 10,000+ packets/sec | 1M+ packets/sec |
| Threat Detection Latency | <100ms average | <50ms average |
| ML Inference Time | <50ms per prediction | <10ms per prediction |
| Concurrent Dashboard Users | 100+ users | 1000+ users |
| Data Retention | 30 days default | 1+ year |
| API Throughput | 1000+ req/sec | 10,000+ req/sec |
- π System Metrics: CPU, memory, disk, network utilization
- π Service Health: Real-time component status monitoring
- β‘ Performance Tracking: Response times, throughput, error rates
- π― ML Model Metrics: Accuracy, precision, recall, F1-score tracking
- π Business Metrics: Threat detection rates, false positive analysis
- π§ Email Notifications: SMTP-based alerting with templates
- π¬ Slack Integration: Real-time notifications and bot commands
- π Webhook Support: Custom webhook integrations for any platform
- π¨ SIEM Integration: Splunk, QRadar, ArcSight, Sentinel compatibility
- π± Mobile Alerts: Push notifications and mobile app support
# Development setup
git clone https://github.com/yashab-cyber/shadow-wall.git
cd shadow-wall
# Install development dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt
# Setup pre-commit hooks
pre-commit install
# Run tests
pytest tests/ -v --cov=src --cov-report=html
# Code quality checks
black src/ tests/
isort src/ tests/
flake8 src/ tests/
mypy src/- π§ͺ Unit Tests: 95%+ code coverage with pytest
- π Integration Tests: End-to-end system testing
- β‘ Performance Tests: Load testing with locust
- π‘οΈ Security Tests: SAST, DAST, dependency scanning
- π³ Container Tests: Docker image security scanning
# Docker
docker run -d -p 8081:8081 shadowwall/shadowwall-ai:latest
# Docker Compose
docker-compose up -d
# Kubernetes
kubectl apply -f k8s/- AWS: EKS, ECS, EC2 with CloudFormation templates
- Azure: AKS, Container Instances, VM Scale Sets
- GCP: GKE, Cloud Run, Compute Engine
- Multi-Cloud: Terraform modules for hybrid deployment
- High Availability: Multi-node clustering with load balancing
- Disaster Recovery: Automated backup and restore capabilities
- Scalability: Horizontal scaling with auto-scaling policies
- Compliance: Built-in compliance reporting and audit trails
- π Installation Guide
- βοΈ Configuration Reference
- π API Documentation
- π Deployment Guide
- π§ Troubleshooting
- π‘οΈ Security Best Practices
- π§ ML Model Documentation
- π― Honeypot Setup Guide
We welcome contributions from the community! Please read our guidelines:
- π Contributing Guidelines
- π Code of Conduct
- π‘οΈ Security Policy
- π Bug Report Template
- π‘ Feature Request Template
- π Bug Reports: Help us identify and fix issues
- π‘ Feature Requests: Suggest new capabilities
- π§ Code Contributions: Submit pull requests
- π Documentation: Improve guides and tutorials
- π§ͺ Testing: Add tests and improve coverage
- π Translations: Help internationalize the platform
- π¨ UI/UX: Enhance user interface and experience
This project is licensed under the MIT License - see the LICENSE file for details.
Security is our top priority. Please read our Security Policy for:
- π Vulnerability reporting procedures
- π Security bug bounty program
- π Emergency security contacts
- π‘οΈ Security best practices
| Resource | Link | Description |
| π Issues | GitHub Issues | Bug reports and feature requests |
| π¬ Discussions | GitHub Discussions | Community Q&A and ideas |
| π Documentation | Read the Docs | Comprehensive documentation |
| π¦ Twitter | @shadowwall_ai | Latest updates and news |
| π§ Email | security@shadowwall-ai.com | Security and enterprise inquiries |
| π¬ Discord | Join Server | Real-time community chat |
- π Open Source Community: For the incredible tools and libraries
- π¬ Security Researchers: For vulnerability reports and insights
- π₯ Contributors: For making this project better every day
- π‘οΈ Users: For trusting ShadowWall AI with their security
- π’ Enterprise Partners: For feedback and use case validation
ShadowWall AI is an open-source project developed by passionate security researchers. Your support helps us:
- π Accelerate development of new AI models
- π Enhance security features and threat detection
- π Create educational resources for the community
- π Support contributors and maintain infrastructure
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οΏ½ Cryptocurrency
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π³ PayPal
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βΏ Bitcoin
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π§ Contact
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Yashab Alam - Founder & CEO of ZehraSec
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π» Development
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πΌ Professional
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πΈ Personal
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π§ Contact
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π ZehraSec Official Channels
Where Artificial Intelligence Meets Cybersecurity Excellence
β Star this repository β’ π Try the Demo β’ π Read the Docs
Made with β€οΈ by Yashab Alam & the cybersecurity community
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