Malware Behavior Detection and Correlation

Here are the initial instructions and explanation of the lab environment. Read this while your environment is busy creating itself from nothing. Yes, this violates physics; we know. How fun!

In this challenge, you will ingest pre-captured Ubuntu logs (auth.log, syslog, and audit.log) into an ELK stack. To ensure proper ingestion into Elasticsearch, you will configure Filebeat to parse these logs. Then, verify the ingested data using Elasticsearch API calls and set up Kibana data views for comprehensive log analysis. This challenge emphasizes hands-on practice with ELK stack configuration and log ingestion—key skills for monitoring and analyzing system activity.

In this challenge, you will analyze logs to detect potential malware activity. Examine logs for unusual authentication attempts, suspicious command executions, and signs of lateral movement. Using Kibana, you will query specific events such as repeated failed login attempts or privilege escalation commands to identify Indicators of Compromise (IOCs). This challenge emphasizes the importance of correlating events across logs using timestamps to reconstruct potential attack patterns and detect malicious activity.

In this challenge, you will create visualizations in Kibana to enhance malware detection and track activity across logs. You will build bar charts, pie charts, and line graphs to represent key events, including failed logins, suspicious commands, and outbound network activity. After creating these visualizations, you will consolidate them into a dynamic dashboard for streamlined monitoring and analysis. By completing this challenge, you will learn how to leverage visualization tools for actionable insights, improving your ability to detect and respond to malware-related incidents in real time.

Welcome to the final challenge! This is your last chance to experiment in the environment. Clicking Finish Lab will end this little world that flittered into existence just for you.