I research machine learning-based intrusion detection for IoT systems, with a focus on streaming evaluation, concept drift, conformal evaluation, adaptive retraining, and explainable AI.
I am a PhD student in Computer and Cyber Sciences at Augusta University. My work sits at the intersection of cybersecurity, machine learning, IoT systems, digital forensics, and real-time intrusion detection.
My current research focuses on building intrusion detection systems that can operate under realistic streaming conditions, where traffic distributions shift over time and models need to adapt without collapsing under retraining overhead.
I also teach introductory Python as a graduate teaching assistant and work as a Senior Software Engineer at Kart Chaser, where I work with a team building video live-streaming applications deployed on web, Android, iOS and smart TVs.
- Streaming IoT intrusion detection
- Conformal evaluation for drift detection
- Adaptive retraining and adaptive chunking
- Explainable AI for security systems
- Multimodal security data pipelines
- Cloud and live-streaming infrastructure
- Python tooling, automation, and research software
My research includes work on:
- IoT malware and attack behavior
- ML-based network anomaly detection
- BGP security policy analysis
- IoT device fingerprinting and authentication
- Privacy and security implications in consumer technologies
- Vault apps and gray-zone digital forensics
For a more complete list, visit my website: sethbarrett.xyz
- Website: sethbarrett.xyz
- GitHub: @sethbarrett50
- DFAIR Lab: DFAIR-LAB-Augusta