Detection: GCP Unusual Number of Failed Authentications From Ip

Updated Date: 2024-09-30 ID: bd8097ed-958a-4873-87d9-44f2b4d85705 Author: Bhavin Patel, Splunk Type: Anomaly Product: Splunk Enterprise Security

Description

The following analytic identifies a single source IP failing to authenticate into Google Workspace with multiple valid users, potentially indicating a Password Spraying attack. It uses Google Workspace login failure events and calculates the standard deviation for source IPs, applying the 3-sigma rule to detect unusual failed authentication attempts. This activity is significant as it may signal an adversary attempting to gain initial access or elevate privileges. If confirmed malicious, this could lead to unauthorized access, data breaches, or further exploitation within the environment.

Search

1`gws_reports_login` event.type = login event.name = login_failure
2| bucket span=5m _time 
3| stats  dc(user_name) AS unique_accounts values(user_name) as tried_accounts values(authentication_method) AS authentication_method by _time, src 
4| eventstats  avg(unique_accounts) as ip_avg , stdev(unique_accounts) as ip_std by _time 
5| eval  upperBound=(ip_avg+ip_std*3) 
6| eval  isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
7| where isOutlier =1
8| `gcp_unusual_number_of_failed_authentications_from_ip_filter`

Data Source

Name	Platform	Sourcetype	Source
Google Workspace login_failure	N/A	`'gws:reports:admin'`	`'gws:reports:admin'`

Macros Used

Name	Value
gws_reports_login	`sourcetype=gws:reports:login`
gcp_unusual_number_of_failed_authentications_from_ip_filter	`search *`

gcp_unusual_number_of_failed_authentications_from_ip_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK

+ Kill Chain Phases

+ NIST

+ CIS

- Threat Actors

ID	Technique	Tactic
T1586	Compromise Accounts	Resource Development
T1586.003	Cloud Accounts	Resource Development
T1110	Brute Force	Credential Access
T1110.003	Password Spraying	Credential Access
T1110.004	Credential Stuffing	Credential Access

KillChainPhase.EXPLOITAITON

KillChainPhase.WEAPONIZATION

NistCategory.DE_AE

Cis18Value.CIS_10

APT29

APT28

APT38

APT39

APT41

Agrius

DarkVishnya

Dragonfly

Ember Bear

FIN5

Fox Kitten

HEXANE

OilRig

Turla

APT28

APT29

APT33

Agrius

Chimera

Ember Bear

HEXANE

Lazarus Group

Leafminer

Silent Librarian

Chimera

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting	Value
Disabled	true
Cron Schedule	`0 * * * *`
Earliest Time	`-70m@m`
Latest Time	`-10m@m`
Schedule Window	`auto`
Creates Risk Event	True

This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

Implementation

You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. We would also recommend tuning the detection by adjusting the window span and unique_accounts threshold values according to your environment. Specifically, this analytic leverages the User log events.

Known False Positives

No known false positives for this detection. Please review this alert

Associated Analytic Story

GCP Account Takeover

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
Unusual number of failed console login attempts (Count: $unique_accounts$) against users from IP Address - $src$	54	60	90

The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	✅ Passing	N/A	N/A	N/A
Unit	✅ Passing	Dataset	`gws_login`	`gws:reports:login`
Integration	✅ Passing	Dataset	`gws_login`	`gws:reports:login`

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

Source: GitHub | Version: 3