| convert timeformat="%Y-%m-%d %T" ctime(6h_latest_time) AS 6h_latest_time ]ġ0. | convert timeformat="%Y-%m-%d %T" ctime(6h_earliest_time) AS 6h_earliest_timeĩ. [ search index=web_idx sourcetype=web_st (AuthType="AuthInvalid" OR AuthType="AuthReject" ORĪuthType="Lockout") | stats count as 6h_total_fail_count count(eval(AuthType="AuthInvalid")) as 6h_invalid_countĬount(eval(AuthType="AuthReject")) as 6h_reject_count count(eval(AuthType=“Lockout")) asĦh_lockout_count earliest(_time) as 6h_earliest_time latest(_time) as 6h_latest_timeĨ. | convert timeformat="%Y-%m-%d %T" ctime(latest_time) AS latest_timeĦ. | convert timeformat="%Y-%m-%d %T" ctime(earliest_time) AS earliest_timeĤ. This use case detects large increase percentages in various brute force statistics over different periods of time (the last 30 minutes, compared to a 30 minute period 6 hours ago).ĪuthType="Lockout") stats count as total_fail_count count(eval(AuthType="AuthInvalid")) as invalid_count count(Įval(AuthType="AuthReject")) as reject_count count(eval(AuthType=“Lockout")) as lockout_countĮarliest(_time) as earliest_time latest(_time) as latest_timeģ. Percent_denied as "Percent Denied" Brute Force Increase Percentage "Successful Logins", challenge_count as "Amount of Challenges", lockout_count as “Locked Attempts”, Username Attempts", reject_count as "Valid Username / Invalid Password Attempts", accept_count as | rename client_ip as "Client IP", total_count as "Total Count", invalid_count as "Invalid | eval percent_denied = round(((reject_count + attempt_count) / total_count) * 100, 2)Ĩ. | where invalid_count > invalid_thresh OR reject_count > reject_threshĦ. (AuthType="AuthReject")) as reject_count count(eval(AuthType ="AuthAccept")) asĪccept_count count(eval(AuthType="AuthChallenge")) as challenge_count count(eval(AuthType=“ĥ. | stats count as total_count count(eval(AuthType="AuthInvalid")) as invalid_count count(eval index=web_idx sourcetype=web_st (AuthType="AuthInvalid" OR AuthType="AuthReject" ORĪuthType="AuthAccept OR AuthType="AuthChallenge" OR AuthType="Lockout")Ģ. This use case is intended to detect source IPs that are exceeding a high threshold of rejected and/or invalid logins.ġ. Example Web Application Log Format (CA SiteMinder) Additionally having the channel (web or mobile) and device information (cookie or hardware ID) can provide extra insight for reporting. At a minimum, they should contain the timestamp, source IP, user account, type of authentication event and the result. Specifically, these logs should include all authentication related events for your website/web applications. In general, web application logs are a good place to start. This, of course, will be dependent upon the your organization's specific use case goals and the technologies and applications being used. These use cases can be built using the standard Splunk SPL and presented as dashboards or saved as scheduled searches that trigger alerts. If your organization utilizes Splunk Enterprise Security, these use cases would work great as correlation searches that trigger notable events. The only true requirement is having the necessary data ingested (and correctly parsed) in your Splunk Enterprise deployment. Some brute force attacks utilize dictionary words and lists of common passwords in order to increase their success rates. Various types of automated software and cracking tools are used to generate a large number of consecutive guesses. Defining Brute ForceĪ brute force attack is a trial and error method used to discover a password by systematically trying every possible combination of letters, numbers, and symbols until the correct combination is found. Let’s explore a few brute force detection use cases that can easily be built using Splunk. If these attacks are not detected and addressed in a timely manner they can lead to theft of intellectual property and personally identifiable information, significant financial losses, and irreversible damage to a business’s reputation. In terms of impact, brute force attacks are a very serious threat capable of affecting millions of accounts. Although this form of attack has been around for many years, it remains one of the most popular and widely used password-cracking methods. One of the longest-standing and most common challenges to information security and web development teams is the brute force attack. As a leader in the Operational Intelligence and Middleware space, Function1 not only designed the base architecture for some of the largest Splunk deployments in the world today, but also helped to develop the standard for enterprise class governance and data onboarding. In this guest blog, Naveed Krabbe (Senior Operational Intelligence Consultant with Splunk Partner Function1 ) examines leveraging Splunk to detect brute force attacks.
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