Frontend Load Test

Frontend Load Test



Meteor can be resource hungry. This happened during your pen-test track and you were the only 1 operator. That said, it is unlikely a single Case can accumulate that many events, without you repeatedly "attacking" the target.
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  • At what point does the wekan boards stall?
The screenshot below is the resource usage statistics for Wekan when the card score was 300.
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When there is a low number of unclosed closes, the resource usage in Wekan is low.
However, loading issues start to occur when there is a large accumulation of unclosed cases. When the total score of the card reached 22000 on my OpenEDR backend VM, the Wekan webpage was stuck in a loading loop when I attempted to refresh the page. It took around 10 minutes for the page to eventually load finish.
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The screenshot below is the resource usage statistics for Wekan when it started to have loading issues.
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When comparing the resource usage statistics before and after the occurrence of loading issues, we can see that there is a big difference in terms of the CPU and memory usage. Loading issues start to occur when the CPU usage reaches 30% and when memory usage reaches 19%. The higher the number of unclosed cases, the more resources are needed to support Wekan, which then causes the webpage to not load. From this test, it showcases a flaw of Wekan i.e. suffers from performance issues even though the CPU & memory usage is not very high.

Jym's Comments

Basis of Scoring

The higher the score, the more anomalies were sighted on a particular endpoint. In some SOCs, Sev1 or Severity 1 is most serious or highest severity level. I personally find it confusing since in natural language (at least in English); if we prioritise based on increasing severity, so why is 4 less severe than 1?
In my model, Stage 1 is least to worry; eg. Internet kiosks that will reboot to clean state, & automatically triggered by DFPM.exe, thus the wekan-label 'rebooted' to indicate that kiosk had rebooted. If we are watching Windows Servers, & there's a custom logic for let's say scanning, that kind of event will also be Stage 1.
The scoring mirrors the ALC Tactical Model, eg. if we see a new driver file & driver files are typically installed through privileged processes, then we say it is Severity-3 (defenders' POV) & Stage-3 (attackers' POV). A backend script assigns per-anomaly score:

"Threat" Score of 22000!!! is it possible?

Suppose we had a 100-endpoints network, a score of 22000 would mean on average of 220 point endpoint. Stage-2 is set at 20 per anomaly, that means each endpoint has 11 Stage-2 anomalies if we evenly distribute it out for the sake of discussion. Perhaps a Ransomware worm that is burning through a network? Maybe... but the output on the Wekan board will be 100 cases, each with a lot less cards than let's say single case (1 host) with many case-events linked to it.
But is such a scenario of high-case-count but with less cards per case still stalling the UI?

Performance of Dashboards vs Ease-of-Interpretation

These 2 factors are usually related.
The most important lesson that I want you to take home when it comes to frontend design is: Information Seeking Mantra.
The Wekan loading-performance issue is secondary to a certain extend, since if I had done some more processing to explain the cases further, so as to minimise the loading of so much data. Unfortunately with talent-constraint, I took the easier way out to link raw events to the case, & let the humans to decide after they acquired a framework of mental models.
There's another interesting problem related to this topic: to what extent do we want the machine or "black-box" to decide for us? The major pain-point with many detection products is that many fail to explain why it emitted "BAD" alerts. Much of the events, decision algorithms & so on are hidden from operations' view (in the name of Intellectual Property rights), which in turns lead to "trust issues".
Recorded Future wrote a good entry on this topic:
Carrot2 search exemplifies some of ISM concepts in action:
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