Deployed Artifacts

Enterprise Network Simulation & Security Architecture (FanNet)

Designed and deployed a realistic, large-scale enterprise network simulation using Proxmox VE and Cisco Modeling Labs (CML). The project emulated a production environment to transition a flat, vulnerable network into a secure, segmented infrastructure. It features a modern security stack (Wazuh, Suricata, ModSecurity), high-availability web clusters, and a custom-developed web application hosted within a hardened DMZ.

Technologies & Tools

Proxmox VE Cisco CML Wazuh XDR Suricata ModSecurity HAProxy/Nginx Cisco ASA Linux/Windows
1. Enterprise-Level Simulation & Architecture
  • Hybrid Virtualization: Leveraged CML for network hardware and Proxmox VE for heavy VMs (Windows DC, ELK Stack).
  • Zone Segmentation: Engineered strict Three-Tier Architecture (External, DMZ, Internal) using VLANs.
  • Defense in Depth: Layered security using Cisco ASA (edge) and pfSense (internal).
2. Advanced Threat Detection (XDR/SIEM)
  • Unified XDR (Wazuh): HIPS, FIM, and log analysis across all virtual endpoints.
  • Network IDS: Suricata for real-time traffic analysis and malware C2 detection.
  • Centralized Alerting: Correlated Suricata alerts with system logs via Wazuh.
3. Web Security & Traffic Management
  • WAF: ModSecurity on Nginx/Apache to block SQLi and XSS.
  • High Availability: HAProxy/Nginx managing traffic across redundant servers.
  • Automated Defense: Custom Fail2Ban scripts to auto-ban IPs triggering WAF rules.
4. Secure Web Development (Game)
  • Application: "FanNet Run" browser game hosted in hardened DMZ.
  • Hardening: Protected by ModSecurity; inputs sanitized.
  • Redundancy: Active-passive failover for instant traffic rerouting.

CTF Lab: Vulnerable Debian VM

Built a multi-stage vulnerable VM for offensive security training. Includes privilege escalation paths, misconfigurations, and weak credentials for real-world simulation.

Debian Bash Exploitation
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Research: Adversarial AI

Authored "Adversarial Intelligence: Comparative Robustness of AI-Based Threat Detection". Analysis of ML-based threat detection resilience against evasion attacks.

Research Machine Learning Defense
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SOC-in-a-Box

Home Lab Security Operations Center using pfSense, ELK Stack, and Wazuh. Monitored live traffic and successfully detected brute-force attacks and malware signatures.

ELK Stack Wazuh pfSense
View on GitHub