Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
608 detections
Filters
Last updated
All Time
Detection languages
346
84
61
42
40
Contributors
155
87
50
48
42
Categories
348
215
150
124
82
Platforms
197
190
120
88
75
Products / Services
169
92
45
44
43
MITRE Techniques
18,123
15,639
12,581
8,394
6,215
CVEs
10
5
1
1
1
IDS Classtypes
35
4
4
3
IDS Protocols
35
6
1
1
Detects failed AWS console login attempts due to authentication failures. Monitoring these events is crucial for identifying potential brute-force attacks or unauthorized access attempts to AWS accounts.
Detects activity related to high-value (VIP) accounts, including:
- Azure AD account modifications
- Conditional Access policy changes affecting the user
- Role assignment changes
- Mailbox rule creation/modification
- Successful sign-ins
Useful for tracking executive account abuse, targeted attacks, or policy misconfigurations.
- Azure AD account modifications
- Conditional Access policy changes affecting the user
- Role assignment changes
- Mailbox rule creation/modification
- Successful sign-ins
Useful for tracking executive account abuse, targeted attacks, or policy misconfigurations.
An attacker knows a users login credentials for an account with MFA enabled. The attacker overloads the user with MFA requests hoping the user will accept one of them. This detection looks for a successful MFA completion that follows a stream of failed MFA requests within the past hour.
This alert indicates that a user has successfully passed MFA after a stream of login failures. This activity may indicate that an attacker has successfully brute forced an accounts credentials and has passed MFA.
Detects scenarios where an attacker attempts to enumerate potential existing SQL users, resulting in failed logins with unexisting or invalid accounts.
Detects the execution of 'dscl . authonly' by a script interpreter, a technique used by Odyssey Stealer to validate user passwords obtained from a malicious dialog prompt.
This rule detects suspicious Okta login attempts where a user successfully passes the initial authentication factor but then fails or abandons a subsequent Multi-Factor Authentication (MFA) challenge, originating from a new IP address, device (user agent), and city combination. It filters out events from known corporate VPN IP addresses to reduce false positives.
This detection identifies suspicious authentication failure patterns that may indicate brute-force attacks. It detects four common scenarios:
- Repeated failed login attempts to a single account from a single IP address.
- Distributed login attempts to a single account from multiple IPs.
- A single IP address targeting multiple accounts.
- Broad-scale attempts involving multiple accounts and multiple IPs in a short timeframe.
This rule focuses on failed login attempts only and is intended for early brute-force detection and threat hunting.
Note:
Consider tuning the TimeWindow (default: 10 minutes) and Threshold (default: 5 attempts) values to reflect your organization’s normal login activity. A more permissive or stricter threshold may be necessary depending on your environment (e.g., high-traffic services, mobile users, VPNs).
Includes both a readable union-based version and a compact case()-based variant for performance-conscious deployments.
- Repeated failed login attempts to a single account from a single IP address.
- Distributed login attempts to a single account from multiple IPs.
- A single IP address targeting multiple accounts.
- Broad-scale attempts involving multiple accounts and multiple IPs in a short timeframe.
This rule focuses on failed login attempts only and is intended for early brute-force detection and threat hunting.
Note:
Consider tuning the TimeWindow (default: 10 minutes) and Threshold (default: 5 attempts) values to reflect your organization’s normal login activity. A more permissive or stricter threshold may be necessary depending on your environment (e.g., high-traffic services, mobile users, VPNs).
Includes both a readable union-based version and a compact case()-based variant for performance-conscious deployments.
The analytic identifies potential Remote Desktop Protocol (RDP) brute force attacks by monitoring network traffic for RDP application activity. It detects potential RDP brute force attacks by identifying source IPs that have made more than 10 connection attempts to the same RDP port on a host within a one-hour window. The results are presented in a table that includes the source and destination IPs, destination port, number of attempts, and the times of the first and last connection attempts, helping to prioritize IPs based on the intensity of activity.
This analytic detects repeated blocked connection attempts from the same initiator to the same responder within a short time window. It leverages Cisco Secure Firewall Threat Defense logs and identifies connections where the action is set to Block, and the number of occurrences reaches or exceeds a threshold of ten within a one-minute span. This pattern may indicate a misconfigured application, unauthorized access attempts, or early stages of a brute-force or scanning operation. If confirmed malicious, this behavior may represent an attacker probing the network, attempting lateral movement, or testing firewall rules for weaknesses.
This KQL detection rule identifies suspicious login attempts in the SigninLogs dataset where a sign-in was blocked because it originated from an IP address flagged for malicious activity.
The rule specifically focuses on cases where valid credentials or token claims were used during the authentication process.
The rule specifically focuses on cases where valid credentials or token claims were used during the authentication process.
KQL Query from file: Password Spraying Detection in Active Directory
KQL Query from file: Microsoft Entra Identity Attack Threat Detection
KQL Query from file: NEW Real-time Anomalous Token Detection
KQL Query
KQL Query
KQL Query from file: Conditional Access Report
KQL Query from file: Azure CLI Spray - ASN 53667
KQL Query from file: Threat hunting voice phishing for Teams
KQL Query from file: MailItemsAccessed Defense
KQL Query from file: Hunting fasthttp Bruteforce Campaign
Page 22 of 29