You are investigating a potential beaconing pattern. You have identified a suspect destination IP. Which SPL command sequence is most appropriate to calculate the frequency of connections to this IP to validate the beaconing hypothesis?
Trap 1: search dest_ip=x.x.x.x | rare dest_ip
rare returns the least common values of dest_ip, not connection frequency over time, so it cannot reveal regular beacon intervals. It is tempting because rare is designed for surfacing unusual field values, which suits finding anomalous hosts or rare user agents, but not measuring periodic timing.
Trap 2: search dest_ip=x.x.x.x | timechart span=1h count
timechart span=1h buckets events into hourly counts, so sub-hour beacon intervals of seconds or minutes are averaged away and periodicity is lost. It is tempting because timechart is built for visualising event volume trends, which suits daily or hourly traffic baselining, not validating short-interval beaconing.
Trap 3: search dest_ip=x.x.x.x | stats count by _time
stats count by _time groups events sharing an identical timestamp, and since _time has second or millisecond granularity, nearly every event lands in its own group, producing counts of one. It is tempting because stats count by a field is a standard aggregation, but _time is not a categorical field.
- A
search dest_ip=x.x.x.x | rare dest_ip
Why it fails: rare returns the least common values of dest_ip, not connection frequency over time, so it cannot reveal regular beacon intervals. It is tempting because rare is designed for surfacing unusual field values, which suits finding anomalous hosts or rare user agents, but not measuring periodic timing.
- B
search dest_ip=x.x.x.x | streamstats current=f window=2 global=f last(_time) as prev_time | eval diff=_time - prev_time
streamstats with window=2 and current=f computes the gap between consecutive connection timestamps to the suspect IP, exposing regular intervals characteristic of beaconing. This directly satisfies the stem's requirement to calculate connection frequency and validate the beaconing hypothesis.
- C
search dest_ip=x.x.x.x | timechart span=1h count
Why it fails: timechart span=1h buckets events into hourly counts, so sub-hour beacon intervals of seconds or minutes are averaged away and periodicity is lost. It is tempting because timechart is built for visualising event volume trends, which suits daily or hourly traffic baselining, not validating short-interval beaconing.
- D
search dest_ip=x.x.x.x | stats count by _time
Why it fails: stats count by _time groups events sharing an identical timestamp, and since _time has second or millisecond granularity, nearly every event lands in its own group, producing counts of one. It is tempting because stats count by a field is a standard aggregation, but _time is not a categorical field.