Significant Increase in Interactive Logons

Description

Typically non-admin users will only interactively log into one system per day. A user who starts loggin into many can indicate account compromise and lateral movement. (MITRE CAR Reference)


Use Case

Advanced Threat Detection

Category

Lateral Movement

Security Impact

By monitoring the number of interactively logged in users to assets, security teams can identify anomalies that may indicate the compromise of an asset or credentials. A spike in users on a particular asset could be an indicate that the asset was compromised and additional system level user accounts are being created for malicious purposes, or if they are valid credentials in AD that accounts have been compromised and the adversary is testing the accounts against a particular asset or groups of assets to test and or escalate privileges to gain deeper access to critical assets and infrastructure.

Alert Volume

Low (?)

SPL Difficulty

Medium

Journey

Stage 1

MITRE ATT&CK Tactics

Lateral Movement
Privilege Escalation
Persistence

MITRE ATT&CK Techniques

Lateral Movement
Valid Accounts
Remote Services

MITRE Threat Groups

APT18
APT28
APT3
APT32
APT33
APT39
APT41
Carbanak
Dragonfly 2.0
FIN10
FIN4
FIN5
FIN6
FIN8
GCMAN
Leviathan
Night Dragon
OilRig
PittyTiger
Soft Cell
Stolen Pencil
Suckfly
TEMP.Veles
Threat Group-1314
Threat Group-3390
menuPass

Kill Chain Phases

Installation
Actions on Objectives

Data Sources

Windows Security

   How to Implement

Implementation of this example (or any of the Time Series Spike / Standard Deviation examples) is generally pretty simple.

  • Validate that you have the right data onboarded, and that the fields you want to monitor are properly extracted. If the base search you see in the box below returns results.
  • Save the search to run over a long period of time (recommended: at least 30 days).

For most environments, these searches can be run once a day, often overnight, without worrying too much about a slow search. If you wish to run this search more frequently, or if this search is too slow for your environment, we recommend using a summary index that first aggregates the data. We will have documentation for this process shortly, but for now you can look at Summary Indexing descriptions such as here and here.

   Known False Positives

This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately a spike in the number we're monitoring... it's nearly impossible for the math to lie. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.

How you handle these alerts depends on where you set the standard deviation. If you set a low standard deviation (2 or 3), you are likely to get a lot of events that are useful only for contextual information. If you set a high standard deviation (6 or 10), the amount of noise can be reduced enough to send an alert directly to analysts.

   How To Respond

When this search returns values, initiate your incident response process and capture the event times, the user account and systems, process and other pertinent information. Contact the owners of the systems. If it is authorized behavior, document that this is authorized and by whom. If not, the user credentials may have been used by another party and additional investigation is warranted.

   Help

Significant Increase in Interactive Logons Help

This example leverages the Detect Spikes (standard deviation) search assistant. Our dataset is an anonymized collection of Windows Logon events, filtered to interactive logon types (Local: 2, RemoteInteractive: 10, Cached Local: 11). For this analysis, we are tracking the number of unique hosts the user has interactively logged into per day 'dc(host) by user _time'. Then we calculate the average, standard deviation, and the most recent value, and filter out any users where the most recent is within the configurable number of standard deviations from average.

SPL for Significant Increase in Interactive Logons

Demo Data

First we pull in our demo dataset.
Bucket (aliased to bin) allows us to group events based on _time, effectively flattening the actual _time value to the same day.
Finally, we can count and aggregate per user, per day.
calculate the mean, standard deviation and most recent value
calculate the bounds as a multiple of the standard deviation

Live Data

First we pull in our dataset of Windows Authentication specifying Interactive logon types.
Bucket (aliased to bin) allows us to group events based on _time, effectively flattening the actual _time value to the same day.
Finally, we can count and aggregate per user, per day.
calculate the mean, standard deviation and most recent value
calculate the bounds as a multiple of the standard deviation