Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects network communication, email interaction, or identity logon events related to known spoofed IC3 (Internet Crime Complaint Center) domains used in business email compromise (BEC) campaigns. The detection covers multiple telemetry sources including email URL information, device network events, and identity logon logs to identify attempts to interact with fraudulent portals.
Detects successful authentication events to the Cisco Secure Firepower Management Center (FMC) web management interface involving low-privileged or read-only user accounts. This pattern is potentially associated with exploitation of vulnerabilities such as CVE-2026-20316, which may be leveraged for privilege escalation or unauthorized access.
Detects successful authentication events to the Cisco Secure Firepower Management Center (FMC) web management interface involving low-privileged or read-only user accounts. This pattern is potentially associated with exploitation of vulnerabilities such as CVE-2026-20316, which may be leveraged for privilege escalation or unauthorized access.
Identifies service principals or managed identities assigned high-privilege roles or permissions that exhibit anomalous access patterns by interacting with cloud resources not seen in the established historical baseline.
Detects high-risk account modifications, such as adding MFA methods or OAuth consent grants, occurring shortly after a user sign-in. This pattern is indicative of post-AiTM session theft (e.g., NovaCookies) where an adversary uses a stolen session token to modify account settings for persistence or MFA bypass.
Detects Microsoft 365 sign-in events where a session token is reused without a fresh MFA challenge, originating from known residential proxy service providers. This behavior is indicative of threat actors using stolen session tokens (Session Replay) to bypass MFA protections.
Detects the creation of Windows services immediately following a network logon event (Logon Type 3) using NTLM or Kerberos. The rule monitors for service names or file paths characteristic of PsExec-style remote execution tools, specifically focusing on suspicious paths (e.g., Temp, UNC paths) or file extensions (e.g., .bat, .ps1, .tmp) used by attackers to execute payloads laterally.
Detects anomalous, non-interactive sign-in activity in Microsoft 365 that suggests session cookie replay (Pass-the-Cookie). The rule correlates successful, MFA-validated interactive sign-ins against subsequent, non-interactive sign-ins that lack MFA, occur from a different device and IP address within a short time window, and bypass standard interactive re-authentication.
Detects anomalous remote interactive or network logons where the specific account and remote host/IP combination has not been observed in the previous 14 days. The rule correlates these new login events with subsequent process execution on the destination host within a short window (5 minutes) to identify potential lateral movement where an adversary uses valid credentials to log in and immediately execute commands.
This rule detects authentication events occurring from devices that have not been seen in the user's history over the previous 14 days and involve non-compliant, unmanaged, or non-Azure AD joined devices. The detection specifically looks for sign-ins that do not utilize interactive credential prompts (token-based), which may indicate an adversary using a stolen session token (e.g., a Primary Refresh Token) from an unrecognized or insecure source.
Detects instances where an Azure AD Connect or MSOL synchronization account performs directory replication actions (DS-Replication-Get-Changes) from a host or at a time inconsistent with its 14-day established baseline. The rule further correlates these anomalies with Azure Audit logs or AzureActivity entries associated with the identity to identify potential cloud-originated abuse or credential compromise.
Detects anomalous authentication behavior where a user account utilizes the same authentication session (LogonId) or material to authenticate across multiple distinct destination hosts in a short timeframe. This rule monitors for both NTLM (Type 3) and Kerberos (TGS/TGT) events, which are indicative of lateral movement techniques such as Pass-the-Hash or Pass-the-Ticket.
Detects anomalous authentication patterns indicative of session token replay attacks. The rule identifies instances where a user successfully performs an MFA-authenticated, interactive sign-in, followed shortly (within 5-60 minutes) by a single-factor sign-in for the same user. The suspicious activity is characterized by a shift to a different IP/ASN, an unmanaged device (non-trusted), and a change in browser or OS identifiers, all while the user's geolocation (city/country) remains consistent. This behavioral pattern is highly consistent with the use of residential proxy infrastructure to replay stolen session tokens, effectively bypassing MFA.
This rule detects user authentication events that deviate significantly from a 14-day historical baseline of known devices, operating systems, browsers, and network locations (ASN). It flags logins where at least two out of four key metadata signals (DeviceId, OS, Browser, ASN) have changed compared to the user's historical pattern, specifically targeting successful authentications that did not trigger a fresh multi-factor authentication (MFA) challenge, potentially indicating token theft or unauthorized session reuse.
Detects Microsoft 365 sign-in events where a session token is reused without a fresh MFA challenge, originating from known residential proxy service providers. This behavior is indicative of threat actors using stolen session tokens (Session Replay) to bypass MFA protections.
This rule monitors Azure AD Sign-in logs to identify 'impossible travel' scenarios, where a user authenticates successfully from two geographically distant locations within a time window (1 hour) that would require travel speeds exceeding a plausible threshold. This behavior is a common indicator of credential compromise or session token replay attacks.
This rule detects anomalous activity involving guest users (external identities) or service principals in Azure AD (Microsoft Entra ID). It baselines typical behavior—including IP addresses, countries, and sign-in frequency—over a 14-day period and alerts when activity in the last 24 hours deviates significantly. This includes sign-ins from new IP addresses or countries, sudden spikes in sign-in volume, after-hours access, or failed authentication attempts, which may indicate account takeover or misuse of third-party vendor access.
This rule detects potential lateral movement by identifying suspicious child processes (such as cmd, powershell, or rundll32) spawned by WmiPrvSE.exe shortly after a remote interactive or network logon on the same device.
Detects inbound emails masquerading as reputable advisory firms (PwC or KPMG) using lookalike sender domains, combined with wire-transfer fraud phrases in the subject line. Correlates with identity logon telemetry for the same fraudulent WhatsApp Business phone number, enabling detection across both email and authentication channels. Note: message body content is not available in the EmailEvents schema, so fraud-phrase matching is subject-only.
Detects a specific adversary pattern termed 'Phantom Deal' where a user is socially engineered into disabling their VPN (by claiming a link won't open), followed immediately by a rapid succession of accesses to the same tracking or canary link from that user account. The rule correlates sign-in events indicating a shift in VPN or MFA context with subsequent bursts of URL clicks.
Detects a specific adversary pattern termed 'Phantom Deal' where a user is socially engineered into disabling their VPN (by claiming a link won't open), followed immediately by a rapid succession of accesses to the same tracking or canary link from that user account. The rule correlates sign-in events indicating a shift in VPN or MFA context with subsequent bursts of URL clicks.
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