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    <title>topic Re: SPL to check for timing attacks in Splunk Enterprise Security</title>
    <link>https://community.splunk.com/t5/Splunk-Enterprise-Security/SPL-to-check-for-timing-attacks/m-p/532128#M9523</link>
    <description>&lt;P&gt;You may find that ML is overkill for this particular use-case.&lt;/P&gt;&lt;P&gt;Consider Apache web logs, for example, which can be configured to include the RequestTimeSeconds, which is the time taken to process a request.&lt;/P&gt;&lt;P&gt;You could then create an alert with something like the following:&lt;/P&gt;&lt;LI-CODE lang="markup"&gt;index=weblogs earliest=-30m@m | eventstats count, avg(RequestTimeSeconds) as avg_rts, stdev(RequestTimeSeconds) as stdev_rts by url | where RequestTimeSeconds&amp;gt;(2*stdev_avg+avg_rts) AND count&amp;gt;10&lt;/LI-CODE&gt;&lt;P&gt;This will give you a list of URLs that have been accessed more than 10 times, and have occurrences where the time to respond has been over 2 standard deviations above the average (per each URL).&lt;/P&gt;&lt;P&gt;You can extend this pattern to looking at SQL logs, authentication logs, etc... You can make a longer time window to develop baselines for, keep track on a daily/weekly/monthly basis, make the limits more than 2 standard deviations above the normal, require more than 10, aggregate based on source/client, etc... You will need to play around with these values to determine values that aren't too noisy, yet detect what you are looking for.&lt;/P&gt;</description>
    <pubDate>Sun, 06 Dec 2020 22:28:13 GMT</pubDate>
    <dc:creator>sduff_splunk</dc:creator>
    <dc:date>2020-12-06T22:28:13Z</dc:date>
    <item>
      <title>SPL to check for timing attacks</title>
      <link>https://community.splunk.com/t5/Splunk-Enterprise-Security/SPL-to-check-for-timing-attacks/m-p/532122#M9520</link>
      <description>&lt;P&gt;Hello fellow splunkers,&lt;/P&gt;&lt;P&gt;I would like to know if someone has come across a way to determine via a splunk query timing attacks, I have read some posts on github pointing out to useful information but still nothing concrete.&lt;/P&gt;&lt;P&gt;I know we could do something with machine learning but not sure how to deal with it deeply in order to check for so.&lt;/P&gt;&lt;P&gt;Thanks so much,&lt;/P&gt;</description>
      <pubDate>Sun, 06 Dec 2020 17:35:20 GMT</pubDate>
      <guid>https://community.splunk.com/t5/Splunk-Enterprise-Security/SPL-to-check-for-timing-attacks/m-p/532122#M9520</guid>
      <dc:creator>jogonz20</dc:creator>
      <dc:date>2020-12-06T17:35:20Z</dc:date>
    </item>
    <item>
      <title>Re: SPL to check for timing attacks</title>
      <link>https://community.splunk.com/t5/Splunk-Enterprise-Security/SPL-to-check-for-timing-attacks/m-p/532126#M9522</link>
      <description>&lt;P&gt;Please explain your use case.&amp;nbsp; What is a "timing attack"?&amp;nbsp; How would you detect one?&lt;/P&gt;</description>
      <pubDate>Sun, 06 Dec 2020 21:41:07 GMT</pubDate>
      <guid>https://community.splunk.com/t5/Splunk-Enterprise-Security/SPL-to-check-for-timing-attacks/m-p/532126#M9522</guid>
      <dc:creator>richgalloway</dc:creator>
      <dc:date>2020-12-06T21:41:07Z</dc:date>
    </item>
    <item>
      <title>Re: SPL to check for timing attacks</title>
      <link>https://community.splunk.com/t5/Splunk-Enterprise-Security/SPL-to-check-for-timing-attacks/m-p/532128#M9523</link>
      <description>&lt;P&gt;You may find that ML is overkill for this particular use-case.&lt;/P&gt;&lt;P&gt;Consider Apache web logs, for example, which can be configured to include the RequestTimeSeconds, which is the time taken to process a request.&lt;/P&gt;&lt;P&gt;You could then create an alert with something like the following:&lt;/P&gt;&lt;LI-CODE lang="markup"&gt;index=weblogs earliest=-30m@m | eventstats count, avg(RequestTimeSeconds) as avg_rts, stdev(RequestTimeSeconds) as stdev_rts by url | where RequestTimeSeconds&amp;gt;(2*stdev_avg+avg_rts) AND count&amp;gt;10&lt;/LI-CODE&gt;&lt;P&gt;This will give you a list of URLs that have been accessed more than 10 times, and have occurrences where the time to respond has been over 2 standard deviations above the average (per each URL).&lt;/P&gt;&lt;P&gt;You can extend this pattern to looking at SQL logs, authentication logs, etc... You can make a longer time window to develop baselines for, keep track on a daily/weekly/monthly basis, make the limits more than 2 standard deviations above the normal, require more than 10, aggregate based on source/client, etc... You will need to play around with these values to determine values that aren't too noisy, yet detect what you are looking for.&lt;/P&gt;</description>
      <pubDate>Sun, 06 Dec 2020 22:28:13 GMT</pubDate>
      <guid>https://community.splunk.com/t5/Splunk-Enterprise-Security/SPL-to-check-for-timing-attacks/m-p/532128#M9523</guid>
      <dc:creator>sduff_splunk</dc:creator>
      <dc:date>2020-12-06T22:28:13Z</dc:date>
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