Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Document Type
Event
Start Date
24-11-2025 4:00 PM
Description
This research work offers a light-weight, end-to-end, syscall-level anomaly detection approach for the Raspberry Pi platform. The proposal involves the collection of around 2000 NORMAL and 200 ANOMALY syscall observation groups using the Linux Auditd safe synthetic generators. The work also utilizes a prototype LLM Explanation Layer, allowing the provision of human-friendly explanations pertaining to identified anomalies leveraging small LLM models like the Gemma-3 1B, Phi-3 Mini, or other sub 1B LLMs employing the Ollama platform. The LLM inference layer in this research work has partial implementations, as the fine-tuning of the model remains to be done.
Included in
GRP-1184 Edge-LLM Anomaly Detection on Raspberry Pi: Syscall Dataset Collection and Prototype LLM Explanation Layer
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
This research work offers a light-weight, end-to-end, syscall-level anomaly detection approach for the Raspberry Pi platform. The proposal involves the collection of around 2000 NORMAL and 200 ANOMALY syscall observation groups using the Linux Auditd safe synthetic generators. The work also utilizes a prototype LLM Explanation Layer, allowing the provision of human-friendly explanations pertaining to identified anomalies leveraging small LLM models like the Gemma-3 1B, Phi-3 Mini, or other sub 1B LLMs employing the Ollama platform. The LLM inference layer in this research work has partial implementations, as the fine-tuning of the model remains to be done.