ioc-detection

A malware analysis platform built in Rust

Walk any memory dump. Find what's hidden. Linux + Windows kernel forensics from a single static Rust binary — no Python required.

A comprehensive integration solution connecting MISP threat intelligence with Wazuh security monitoring for real-time threat detection. This project provides step-by-step instructions for deploying, configuring, and integrating MISP and Wazuh with Sysmon to automatically detect indicators of compromise (IoCs) in your environment.

Shell script to detect TanStack npm supply chain attack indicators (CVE-2026-45321 / GHSA-g7cv-rxg3-hmpx)

Extract useful information from PANOS support file for CVE-2024-3400

One-click enterprise security audit tool in PowerShell. Collects system info, detects IOCs, generates reports in HTML/PDF/CSV.

Lightweight detection tool for the Nx Console kitty backdoor (May 2026)

Windows Event Log Analysis — Incident Response Simulation using Event Viewer (Alfido Tech Task 4)

Network threat detection platform that analyzes packet, firewall, and IOC activity to identify suspicious traffic, score risk, map findings to MITRE ATT&CK, and support SOC investigation workflows.

A Python-based static analysis tool that inspects PDF internal structure to detect malicious JavaScript, obfuscated streams, embedded payloads, and indicators of compromise using object & stream level parsing inspired by pdfid, pdf-parser, peepdf, and qpdf methodologies.

Executive phishing email analysis for VitalCare Health Solutions – includes header inspection, BEC indicators, SPF/DKIM/DMARC checks, malicious attachment & URL analysis, and a stakeholder-ready executive report with findings, impact, and recommendations.

A Python-based CTI system for analyzing IOCs like IPs, domains, URLs, and file hashes using threat intelligence APIs

A comprehensive collection of security log analysis projects and methodologies for detecting threats, credential abuse, and advanced persistent threats (APTs) in enterprise environments. Features detailed forensic investigations of large-scale Windows Security Event Logs using Python-based data analytics and behavioral pattern recognition.

Lightweight log scanner to flag brute-force attempts and high-volume hostile IP activity.

Defensive malware-analysis coursework: a reusable Python static-analysis pipeline (string extraction, MD5/SHA-256 hashing, IoC keyword detection) with full documentation and language-agnostic pseudocode. Simulated labs — no real malware.