Dawg Mann

I'm developing SecureMesh, a distributed AI-powered IoT honeypot platform that captures and analyzes real-world attacks against SSH and IoT services.

My work combines Python, Docker, ESP32, machine learning, and cybersecurity to classify brute-force activity, reconnaissance, malware uploads, and suspicious command execution.

I built a dual-layer network and host intrusion detection system that achieved 92.6% recall on known attacks and an 80% detection rate for unseen zero-day families. I also deployed a highly available Prometheus and Grafana monitoring platform with automated Ansible deployment and centralized alerting.

Through internships at WhiteNoise Academy and Skyway Networks, I've explored LoRa-based local LLM communication, embedded security hardware, and employee management application development. I'm pursuing computer science studies in Sweden with focus areas including cryptography, network security, IoT protocols, AI, and machine learning.