Projects that solves problems.
1. Project: CredCrawler
Overview
a Python-based crawler that monitors Tor (.onion) sites to detect leaked credentials. It routes traffic through the Tor SOCKS5h proxy, then crawls pages (either via Selenium+Firefox for dynamic browsing or via requests for HTTP fetching) and spiders new links in a BFS-like manner. The crawler scans fetched HTML using regex to identify email:password-style patterns. When matches are found, it hashes the credentials with SHA-256 (avoids storing plaintext) and can send real-time alerts via Gmail SMTP.
2. Project: SensoGuard
Overview
SensoGuard is a wearable IoT health monitoring system that fuses multi-sensor data from motion (MPU-6050), load sensing (HX711), and pulse-oximetry (MAX30102) using an ESP8266-based edge device with non-blocking polling for continuous monitoring. The system streams physiological data to a cloud backend where event-driven Supabase Edge Functions perform real-time anomaly detection and automatically trigger alerts for critical conditions such as falls or abnormal vitals. A real-time Next.js dashboard provides remote monitoring and visualization for caregivers. The architecture and system design have been filed for a patent as a hybrid edge-cloud wearable health monitoring solution.
3. Project: Automated Reconnaissance
Overview
This is a automated Domain Reconnaissance Tool that streamlines critical information-gathering steps used in cybersecurity assessments. The system integrates DNS enumeration, subdomain discovery, traceroute analysis, port scanning, SSL inspection, and geolocation into a single workflow. Using APIs, Nmap scripts, and automated web scraping, it collects intelligence and generates a structured PDF reconnaissance report. This tool reduces manual reconnaissance effort while providing security analysts with rapid, actionable insights about a target domain.
4. Project: Forensight
Overview
ForenSight is a cyber triage platform designed to accelerate digital forensic investigations by automating evidence collection, analysis, and threat prioritization. The system integrates disk imaging, hashing, log analysis, and network activity inspection to rapidly surface indicators of compromise. AI/ML-based anomaly detection assigns risk scores to suspicious artifacts, helping investigators focus on high-impact threats first. It also generates structured, exportable forensic reports, enabling faster incident response and collaborative investigations.
