Learn to build and secure a production-grade private cloud using Proxmox VE. Covers network segmentation, backup strategies, security hardening, and resource management with real homelab implementation lessons.

Eight security layers that stopped real attacks in homelab testing: minimal base images, user namespaces, seccomp profiles, network segmentation, and more. Defense-in-depth without Kubernetes overhead.

Real-world guide to monitoring security events in your homelab. Covers Prometheus configuration, Grafana dashboards, and alerting rules for threat detection.

NodeShield enforces SBOMs at runtime using CBOM policies to prevent supply chain attacks. Homelab Docker deployment guide with attack simulations, 98.3% prevention rate, and <1ms overhead.

Deploy PromSketch to optimize slow PromQL queries using sketch-based approximation. Homelab benchmarks show 2-100x speedup on percentile queries.

Deploy and benchmark Wazuh and Graylog SIEM solutions in your homelab. Performance analysis, resource usage, and integration patterns for security monitoring.

Build privacy-first AI lab with local LLMs—run models up to 34B on RTX 3090 (24GB VRAM) with network isolation, traffic monitoring, and real privacy controls.

Implement post-quantum cryptography with CRYSTALS-Kyber and Dilithium—prepare homelab for quantum threats using NIST-approved algorithms.

Deploy Vision-Language-Action models for embodied AI robots—integrate physical world interaction with security considerations for homelab automation.

Build Proxmox high-availability clusters with shared storage and automated failover—implement live migration for zero-downtime homelab maintenance.

Test IoT security with OWASP IoTGoat—practice firmware extraction, API exploitation, and hardware hacking in secure lab environments.

Implement zero trust with VLAN segmentation—secure homelab networks using micro-segmentation and layer 3 firewalls for defense in depth.

Migrate to self-hosted Bitwarden—deploy secure vault with backup strategies, SSL certificates, and database encryption for full control.

Deploy Suricata IDS/IPS for real-time network threat detection—configure rule management, performance tuning, and SIEM integration for homelab monitoring.

Harden Docker containers using AppArmor and SELinux for isolation without orchestration overhead. LSM profiles, seccomp filters, and capability dropping at homelab scale.

Deploy DNS-over-HTTPS with Pi-hole and dnscrypt-proxy—encrypt DNS queries for home network privacy and prevent ISP monitoring with DoH implementation.

Deploy local LLMs for privacy-first AI—run language models on homelab hardware with model selection, optimization, and deployment strategies.

Fine-tune LLMs on homelab hardware with QLoRA and 4-bit quantization. Train Llama 3 8B models on RTX 3090 with dataset prep and optimization strategies.

Build security-focused homelab with Proxmox, VLANs, and IDS/IPS—create testing environment for cybersecurity and family data protection.

Secure personal AI experiments with model isolation and network segmentation—protect LLM deployments using privacy controls and threat modeling.

Build Raspberry Pi security projects with Pi-hole, VPN gateway, and honeypots—deploy practical network monitoring and threat detection on budget hardware.

Automate home network security with Python and Ansible—deploy patching, threat detection, and compliance scanning for homelab infrastructure.

Automate security alert analysis using local LLMs (Ollama) for privacy-preserving incident response. Reduce alert fatigue with AI-powered triage without cloud dependencies.

Deploy federated learning across homelab with granular-ball computing—train privacy-preserving models with 82% reduced network transfer.

Monitor GPU power with NVIDIA SMI and Grafana dashboards—reduce ML training electricity costs by 40% using optimization strategies for RTX 3090.

Secure containers with gVisor sandboxing—prevent kernel exploits in Kubernetes clusters while managing 59% startup overhead for untrusted workloads.

Run LLaMA 3.1 on Raspberry Pi with PIPELOAD pipeline inference—achieve 90% memory reduction and deploy 7B models on 8GB edge devices at 2.5 tokens/sec.

Implement privacy-preserving authentication using ZK-SNARKs for homelab SSO. No passwords transmitted, cryptographic proof of identity without revealing credentials.

Automate vulnerability detection in your homelab using Python and the National Vulnerability Database API. Track CVEs, scan dependencies, and integrate with monitoring systems.