LLM-Powered-Monitoring-Agent/PROGRESS.md

Project Progress

Phase 1: Initial Setup

1. Create monitor_agent.py
2. Create config.py
3. Create requirements.txt
4. Create README.md
5. Create .gitignore
6. Create SPEC.md
7. Create PROMPT.md
8. Create CONSTRAINTS.md

Phase 2: Data Storage

9. Implement data storage functions in data_storage.py
10. Update monitor_agent.py to use data storage
11. Update SPEC.md to reflect data storage functionality

Phase 3: Expanded Monitoring

12. Implement CPU temperature monitoring
13. Implement GPU temperature monitoring
14. Implement system login attempt monitoring
15. Update monitor_agent.py to include new metrics
16. Update SPEC.md to reflect new metrics
17. Extend calculate_baselines to include system temps

Phase 4: Troubleshooting

18. Investigated and resolved issue with jc library
19. Removed jc library as a dependency
20. Implemented manual parsing of sensors command output

Phase 5: Network Scanning (Nmap Integration)

21. Add python-nmap to requirements.txt and install.
22. Define NMAP_TARGETS and NMAP_SCAN_OPTIONS in config.py.
23. Create a new function get_nmap_scan_results() in monitor_agent.py:
    • Use python-nmap to perform a scan on the defined targets with the specified options.
    • Return the parsed results.
24. Integrate get_nmap_scan_results() into the main monitoring loop:
    • Call this function periodically (e.g., less frequently than other metrics).
    • Add the nmap results to the combined_data dictionary.
25. Update data_storage.py to store nmap results.
26. Extend calculate_baselines() in data_storage.py to include nmap baselines:
    • Compare current nmap results with historical data to identify changes.
27. Modify analyze_data_with_llm() prompt to include nmap scan results for analysis.
28. Consider how to handle nmap permissions.
29. Improve Nmap data logging to include IP addresses, open ports, and service details.

Phase 6: Code Refactoring and Documentation

30. Remove duplicate pingparsing import in monitor_agent.py.
31. Refactor get_cpu_temperature and get_gpu_temperature to call sensors command only once.
32. Refactor get_login_attempts to use a position file for efficient log reading.
33. Simplify JSON parsing in analyze_data_with_llm.
34. Move LLM prompt to a separate function build_llm_prompt.
35. Refactor main loop into smaller functions (run_monitoring_cycle, main).
36. Create helper function in data_storage.py for calculating average metrics.
37. Update README.md with current project status and improvements.
38. Create AGENTS.md to document human and autonomous agents.

Keeping track of Current Objectives

[x] Improve "high" priority detection by explicitly instructing LLM to output severity in structured JSON format. [x] Implement dynamic contextual information (Known/Resolved Issues Feed) for LLM to improve severity detection.

TODO

• Change baseline calculations to only use integers instead of floats.
• Add a log file that only keeps records for the past 24 hours.
• Log all LLM responses to the console.
• Reduce alerts to only happen between 9am and 12am.
• Get hostnames of devices in Nmap scan.
• Filter out RTT fluctuations below 10 seconds.
• Filter out temperature fluctuations with differences less than 5 degrees.