
network-attack-detection
Advanced detection of port scanning, DoS and malware attacks using Machine Learning techniques

Advanced detection of port scanning, DoS and malware attacks using Machine Learning techniques

A complete Blue Team Cybersecurity Lab featuring pfSense, Suricata, and ELK Stack for network monitoring and threat detection.

Educational demo of CVE-2020-1472 (ZeroLogon) detection using Windows Event Logs and Suricata IDS, plus mitigation via Windows Updates. Includes…

Honeynet Project generic authenticated datafeed protocol

Deploy web honeypots to capture emerging attack data, analyze ModSecurity audit logs via ELK, and share threat intelligence with MISP for…

PowerShell-based security toolkit for small-to-medium enterprises, providing automated alerts, Active Directory hardening, Windows Event Forwarding,…

This project is a SIEM with SIRP and Threat Intel, all in one.

Rust tool to detect cell site simulators on an orbic mobile hotspot

Defensive security demo: seL4 microkernel gateway protecting vulnerable ICS from CVE-2019-14462

Isolated AD/Linux attack lab: exploited CVE-2007-2447 via Metasploit, detected with Wazuh SIEM mapped to MITRE ATT&CK (T1190, T1059)

🔒 Spring4Shell Firewall Defense — Cybersecurity Incident Simulation This project is part of a Cybersecurity Job Simulation I completed in August…

The OWASP SecureTea Project provides a one-stop security solution for various devices (personal computers / servers / IoT devices)

A lightweight, real-time Security Information and Event Management (SIEM) dashboard built using Streamlit. It collects system logs, detects USB and…

📡 🍍Detects activities of PineAP module and starts deauthentication attack (for fake access points - WiFi Pineapple Activities Detection)

Windows honeypot using ProjFS to project decoy files that trigger Event Log and desktop alerts when accessed, with SMB remote session logging for…

OWASP ModSecurity Core Rule Set (CRS) Project (Official Repository)

Small tool to play with IOCs caused by Imageload events

Machine Learning based Intrusion Detection Systems are difficult to evaluate due to a shortage of datasets representing accurately network traffic…