Comparing Zone Names in Splunk for Effective Traffic Analysis

Analyzing network traffic zones in Splunk can be challenging, especially when you need to compare zone names to identify unexpected communications. The initial approach using case and like for comparing zone names, as highlighted in the original query, faces limitations in scalability and accuracy when dealing with complex naming conventions or multiple keywords within zone names. This article explores a more robust and scalable method to Compare Other Words within zone names in Splunk for improved traffic analysis.

The user’s initial attempt aimed to identify traffic between zones that shouldn’t be communicating by checking if keywords like “coke”, “pepsi”, “pepper”, and “sprite” were present in both source and destination zone names. Their approach used a series of case statements with like to extract these keywords and then compare them.

| eval dest_test=case(like(dest_zone ,"%coke%") ,"coke", like(dest_zone ,"%pepsi%") ,"pepsi", like(dest_zone ,"%pepper%") ,"pepper", like(dest_zone ,"%sprite%") ,"sprite", 1<2, "Not found1")
| eval src_test=case(like(src_zone ,"%coke%") ,"coke", like(src_zone ,"%pepsi%") ,"pepsi", like(src_zone ,"%dr-pepper%") ,"dr-pepper", like(src_zone ,"%sprite%") ,"sprite", 1<2, "Not found2")
| eval outcome = if(src_test == dest_test, "match", "no match")
| eval concat_z2z = if(outcome == "no match" , (dest_zone . " : " . src_zone), "Expected"
| where concat_z2z != "Expected"
| table concat_z2z

While this code snippet provides a basic comparison, it has several drawbacks. Firstly, the case statement is processed sequentially. If a zone name contains multiple keywords (e.g., “coke-combo-pepsi”), it might only match the first keyword in the list, leading to inaccurate comparisons. Secondly, adding new keywords requires manually editing the case statement, making it difficult to scale and maintain as the number of hosts and zones grows. Finally, the use of like with wildcard matching % can be inefficient for larger datasets.

A more efficient and scalable solution involves using the rex command with regular expressions to extract keywords from zone names. This allows for more flexible pattern matching and can handle cases where zone names contain multiple keywords. Instead of hardcoding keywords in the query, you can define them in a more maintainable way, such as using a lookup table or a more dynamic approach if needed for a very large and frequently changing environment.

Here’s an improved approach using rex for keyword extraction and comparison:

| rex field=dest_zone "(?<dest_company>(coke|pepsi|pepper|sprite))"
| rex field=src_zone "(?<src_company>(coke|pepsi|pepper|sprite))"
| eval outcome = if(dest_company == src_company, "match", "no match")
| where outcome == "no match" AND isnotnull(dest_company) AND isnotnull(src_company)
| eval unexpected_traffic = dest_zone . " : " . src_zone
| table unexpected_traffic

Explanation of the Improved Query:

1. rex field=dest_zone "(?<dest_company>(coke|pepsi|pepper|sprite))": This command uses regular expression (coke|pepsi|pepper|sprite) to extract any of the specified company names from the dest_zone field. The (?<dest_company>...) part names the extracted value as dest_company.
2. rex field=src_zone "(?<src_company>(coke|pepsi|pepper|sprite))": Similarly, this extracts the company name from the src_zone field and names it src_company.
3. eval outcome = if(dest_company == src_company, "match", "no match"): This compares the extracted company names. If they are the same, it’s considered a “match” (expected traffic); otherwise, it’s a “no match” (potentially unexpected traffic).
4. where outcome == "no match" AND isnotnull(dest_company) AND isnotnull(src_company): This filters the results to show only “no match” cases and ensures that both dest_company and src_company have extracted values (to avoid considering zones without keywords as mismatches).
5. eval unexpected_traffic = dest_zone . " : " . src_zone": This creates a field unexpected_traffic showing the zone-to-zone communication for easier readability.
6. table unexpected_traffic: Finally, this displays the unexpected_traffic field in the output table.

This revised query offers several advantages. It’s more readable, efficient, and scalable. Adding or modifying keywords in the regular expression is straightforward, and regular expressions provide more powerful pattern matching capabilities than simple like statements. By using rex to compare other words (in this case, company names) within the zone names, you gain a more accurate and maintainable solution for analyzing traffic patterns in Splunk and identifying potentially anomalous communications. Remember to adjust the regular expression and keywords to match your specific naming conventions and requirements.