The Logs Were Right: Debugging Your Mental Model in Zeek
When an expected log fails to populate, an alert queue explodes, or a custom script behaves strangely, it’s natural to pause and investigate. Running a high-throughput network monitoring engine like Zeek comes with a learning curve, and practitioners frequently encounter outputs that require deeper troubleshooting. Is a parser failing? Did a script hit an edge case? Or is the system simply responding to something unexpected in the environment?
In our July community Slack discussion, we invited members to share stories about learning from mistakes. What emerged was a reassuring, shared realization: when Zeek seems to act up, it is rarely being unpredictable. Instead, these moments are invitations to explore how the engine handles state, how protocols behave, and how configuration choices play out in production.
When Zeek Does Exactly What You Asked
Zeek operates with literal precision, executing configurations as written. In one community story, an operator was asked by auditors to make their deployment behave more like Snort because the auditors did not think Zeek was effective enough. To satisfy them, the operator changed notices to alarms. Zeek complied instantly, generating 74,000 alerts in just two hours. The auditors were satisfied with the output, even if the sheer volume was overwhelming.
In another story shared during the discussion, a practitioner recalled setting up alert scripts to page a phone, back when text messages carried individual per-message fees. An unexpected surge in alerts quickly generated a steep phone bill. A fellow community member noted that organizations using modern cloud notification services still pay on a per-message basis today. In both scenarios, the engine performed its configured job flawlessly, offering a memorable lesson in managing automation edge cases.
When the Logs Don’t Lie
Another common moment of troubleshooting occurs when log output appears to challenge basic network assumptions. One veteran operator recalled reading logs that initially made no sense, such as seeing non-routable RFC1918 IP addresses on a cluster situated outside the DMZ. In a separate instance, DNS logs showed unexpected activity that turned out to be plain-text passwords being transmitted over DNS TXT records.
In both cases, investigating the traffic confirmed the logs were right, the environment just needed more digging. When log data looks impossible, it’s almost always a reflection of surprising network reality rather than a broken logger.
When the Gap Is Engine Mechanics
Working with offline packet captures brings its own set of subtle learning moments during PCAP replay. One community member shared an experience writing a payload capture plugin and replaying traffic using zeekctl. When no logs were generated, searching the documentation yielded no answers. Reading the source code revealed that Zeek processes certain events only when a new packet arrives. Because replay stopped immediately after the final packet, pending events sat unprocessed until an additional dummy packet was sent to flush the buffer.
A contributor noted a related issue with the input framework: when testing an input reader on a PCAP, the input framework may not finish running asynchronously before Zeek completes reading the PCAP file. Another added that struggling with Zeek’s concept of wall-clock time versus network time is a common hurdle during analysis.
Building the Right Diagnostic Instinct
Gaining confidence with Zeek is not about avoiding mistakes or writing perfect code on the first try. From syntax slip-ups like mixing up assignment operators, to forgetting to strip VLAN tags, or adding items to the intelligence framework without proper filters, scripting gotchas happen to everyone. As a core team member noted in the discussion, it’s completely normal to initially wing it on table sizes or event volumes, as you cannot know ahead of time how logic will hold up across different traffic mixes until you iterate and refine.
The real transformation comes from shifting our troubleshooting posture. When a log looks strange or a script produces unexpected results, the most effective response is curiosity. Every unexpected output is an opportunity to uncover a hidden network behavior, understand engine mechanics, and build a more resilient deployment.
Thank you to the community members who contributed to July’s Learning from Mistakes conversation: Aashish, Chris C., Christian, Dop, Evan, Smoot, Carlos, Trong, Kevin, and Seth.
Our topic for August is Deployment Decisions. We’re curious to learn: What deployment decision has had the biggest impact on how well Zeek works in your environment? Join us in the #topic-of-the-month channel in the Zeek Slack workspace to let us know.