This lab focuses on a small detection engineering experiment. The objective was to write a simple detection rule and evaluate how it behaves when applied to sample log data.
The rule concept was intentionally straightforward. I focused on identifying suspicious PowerShell execution patterns commonly associated with post exploitation activity.
Sample logs were generated in a controlled environment and supplemented with sanitized data. The logs included both normal administrative activity and deliberately suspicious commands.
The first version of the rule detected several relevant events but also triggered on legitimate administrative tasks. This is a common outcome when writing detection logic for flexible tools such as PowerShell.
From there the process became iterative:
- Identify which events are legitimate.
- Understand what differentiates them from malicious activity.
- Refine the rule to reduce false positives.
Several attributes turned out to be useful during tuning:
- The parent process spawning PowerShell.
- Command line flags used during execution.
- The presence of encoded commands.
Each adjustment reduced noise while preserving the ability to detect suspicious behavior.
This exercise highlights an important reality of detection engineering. Writing the first version of a rule is easy. Making it reliable in a real environment requires careful observation, tuning, and documentation.