Switched to SQLlite database

SPEC.md

@@ -108,3 +108,29 @@ The project will be composed of the following files:
 
 ## 7. Testing and Debugging
 The script is equipped with a test mode, that only runs the script once, and not continuously. To enable, change the `TEST_MODE` variable in `config.py` to `True`. Once finished testing, change the variable back to `False`.
+
+## 8. Future Enhancements
+
+### 8.1. Process Monitoring
+
+**Description:** The agent will be able to monitor a list of critical processes to ensure they are running. If a process is not running, an anomaly will be generated.
+
+**Implementation Plan:**
+
+1.  **Configuration:** Add a new list variable to `config.py` named `PROCESSES_TO_MONITOR` which will contain the names of the processes to be monitored.
+2.  **Data Ingestion:** Create a new function in `monitor_agent.py` called `get_running_processes()` that uses the `psutil` library to get a list of all running processes.
+3.  **Data Analysis:** In `analyze_data_locally()`, compare the list of running processes with the `PROCESSES_TO_MONITOR` list from the configuration. If a process from the configured list is not found in the running processes, generate a "high" severity anomaly.
+4.  **LLM Integration:** The existing `generate_llm_report()` function will be used to generate a report for the new anomaly type.
+5.  **Alerting:** The existing alerting system will be used to send alerts for the new anomaly type.
+
+### 8.2. Docker Container Monitoring
+
+**Description:** The agent will be able to monitor a list of critical Docker containers to ensure they are running and healthy. If a container is not running or is in an unhealthy state, an anomaly will be generated.
+
+**Implementation Plan:**
+
+1.  **Configuration:** Add a new list variable to `config.py` named `DOCKER_CONTAINERS_TO_MONITOR` which will contain the names of the Docker containers to be monitored.
+2.  **Data Ingestion:** Create a new function in `monitor_agent.py` called `get_docker_container_status()` that uses the `docker` Python library to get the status of all running containers.
+3.  **Data Analysis:** In `analyze_data_locally()`, iterate through the `DOCKER_CONTAINERS_TO_MONITOR` list. For each container, check its status. If a container is not running or its status is not "running", generate a "high" severity anomaly.
+4.  **LLM Integration:** The existing `generate_llm_report()` function will be used to generate a report for the new anomaly type.
+5.  **Alerting:** The existing alerting system will be used to send alerts for the new anomaly type.

requirements.txt

@@ -4,4 +4,5 @@ discord-webhook
 ollama
 syslog-rfc5424-parser
 python-nmap
-schedule
+schedule
+docker