Detect Suspicious Login Activity (Volume)

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An unusually high number of login attempts from a client can be an indication of a brute force login attack.

This use case recipe identifies clients associated with unusual volumes of failed login attempts.

This use case recipe is provided as a basic example of how automated anomaly detection can be used to detect suspicious login activity. Other recipes, based upon alternative or more complex approaches, may produce more effective detection results.

Use Case Type
Elementary Attack Behavior (EAB) - This use case detects anomalies associated with elementary attack behaviors. Each detected anomaly is assigned a normalized Anomaly Score, and is annotated with values of other fields in the data that have statistical influence on the anomaly. Elementary attack behaviors that share common statistical Influencers are often related to a common attack progression.

Use Case Data Source
Windows AD logs, or Linux system authentication logs

Use Case Recipe

  • For: All relevant authentication log data (filtered as appropriate, possibly including only privileged user accounts, internal IP addresses, etc.)
  • Model: Number of failed login attempts from each client
  • Detect: Unusually high numbers of failed login attempts
  • Compared to: Population of all clients
  • Partition by: Targeted server
  • Exclude: None
  • Duration: Run analysis authentication logs from a period of 2 weeks or longer
  • Related recipes: Run this EAB use case by itself, or along with other login-related EAB's
  • Results: Influencer clients are possible sources of brute force login attacks. Influencer servers are victims.

Input Features and Candidate Influencers

Required field (or similar) Description Example
client A unique identifier for the client system initiating login attempts that is present in the authentication logs (e.g., src_ip,, system.auth.ssh.ip, etc.), mikep, ent.eng.mbp.mikep
user Username associated with login attempts (e.g., system.auth.user) jdoe, johndoe, john.h.doe
server The target server upon which the login attempts are made, server_101,

Example Elasticsearch Index Patterns

  • wsl-*
  • ad-*
  • filebeat-*
  • winlogbeat-*
  • cef-ssh-*

Example Elasticsearch Query

  • query: {"terms":{"system.auth.ssh.event":["Failed","Invalid"],"boost":1}}
  • query_delay: 60s
  • frequency: 300s
  • scroll_size: 1000

Machine Learning Analysis / Detector Config

  • Detector(s): high_count over client partitionfield=server
  • Bucketspan: 10m
  • Influencer(s): client, user, server

The partitionfield=server clause tailors this recipe for detecting brute force attacks targeting an individual server. By removing this clause, the recipe is more likely to detect login attempts across a number of servers.

Recipe ID: WSL-EAB28

Revision: v0.4

Last updated: 5/17/2017