Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
254 detections
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'This query looks at correlating different reconnaissance alerts with interactive logon logs to help analysts investigate initial possible compromise activity'
'This query combines different Storage alerts with CommonSecurityLogs and AuditLogs helping analysts investigate any possible storage related attacks faster
thus reducing Mean Time To Respond'
thus reducing Mean Time To Respond'
'Any Alerts that fired related to a given IpAddress during the range of +6h and -3d'
'Any Alerts that fired related to a given File during the range of +6h and -3d'
'Any Alerts that fired related to a given account during the range of +6h and -3d'
'Any Alerts that fired on a given host during the range of +6h and -3d'
'This query looks for Microsoft Defender AV detections related to Hive Ransomware.
In Microsoft Sentinel the SecurityAlerts table includes only the Device Name of the affected device, this query joins the DeviceInfo table to clearly connect other information such as Device group, ip, logged on users etc. This would allow the Microsoft Sentinel analyst to have more context related to the alert, if available.'
In Microsoft Sentinel the SecurityAlerts table includes only the Device Name of the affected device, this query joins the DeviceInfo table to clearly connect other information such as Device group, ip, logged on users etc. This would allow the Microsoft Sentinel analyst to have more context related to the alert, if available.'
'Anomaly Rules generate events in the Anomalies table. This scheduled rule tries to detect Anomalies that are not usual, they could be a type of Anomaly that has recently been activated, or an infrequent type. The detected Anomaly should be reviewed, if it is relevant enough, eventually a separate scheduled Analytics Rule could be created specifically for that Anomaly Type, so an alert and/or incident is generated everytime that type of Anomaly happens.'
'Surfaces any Defender Alert for Solorigate Events. In Microsoft Sentinel the SecurityAlerts table includes only the Device Name of the affected device, this query joins the DeviceInfo table to clearly connect other information such as
Device group, ip, logged on users etc. This way, the Microsoft Sentinel user can have all the pertinent device info in one view for all the the Solarigate Defender alerts.'
Device group, ip, logged on users etc. This way, the Microsoft Sentinel user can have all the pertinent device info in one view for all the the Solarigate Defender alerts.'
IOC Domain C2
YARA-L
Detect DNS events that indicate communication to a C2 domain in MISP
Detect network connection to an ip address that was only seen for the first time in the past 24 hours
Detect repeated authentication failure with multiple users indicative of a password spray attack.
This analytic aggregates and summarizes alerts from Microsoft Defender for Endpoint. It extracts details such as source, file name, severity, process command line, IP address, registry key, signature, description, and unique ID. The detection is not intended to identify new activity from raw data but rather to leverage existing Microsoft Defender for Endpoint alerts for correlation and risk-based alerting. It filters out alerts with a 'clean' verdict and dynamically maps MITRE techniques and assigns risk scores based on the alert's severity.
This correlation rule identifies when a user exceeds a predefined risk threshold based on multiple suspicious Okta activities within a 24-hour period. It aggregates risk events from "Suspicious Okta Activity," "Okta Account Takeover," and "Okta MFA Exhaustion" analytic stories. The detection is significant as it highlights potentially compromised user accounts exhibiting multiple tactics, techniques, and procedures (TTPs). If confirmed malicious, this activity could indicate a serious security breach, allowing attackers to gain unauthorized access, escalate privileges, or persist within the environment.
The analytic identifies potential lateral movement activities within an organization's Active Directory (AD) environment by correlating multiple analytics from the Active Directory Lateral Movement analytic story within a specified time frame. This is significant for a SOC as lateral movement is a common tactic used by attackers to expand their access within a network, posing a substantial risk. If confirmed malicious, this activity could allow attackers to escalate privileges, access sensitive information, and persist within the environment, leading to severe security breaches.
The analytic detects multiple CrowdStrike LOW severity alerts originating from the same endpoint, indicating a series of minor suspicious activities or policy violations. Specifically, it triggers when an endpoint generates three or more distinct types of LOW severity alerts. These alerts are not immediately critical but should be reviewed to prevent potential threats. They often highlight unusual behavior or low-level risks that, if left unchecked, could escalate into more significant security issues. Regular monitoring and analysis of these alerts are essential for maintaining robust security, as a cluster of different low-severity events can be an early indicator of a more significant compromise or an adversary's reconnaissance activities.
The analytic identifies potential exploitation of Log4Shell CVE-2021-44228 by correlating multiple MITRE ATT&CK tactics detected in risk events. It leverages Splunk's risk data model to calculate the distinct count of MITRE ATT&CK tactics from Log4Shell-related detections. This activity is significant because it indicates a high probability of exploitation if two or more distinct tactics are observed. If confirmed malicious, this activity could lead to initial payload delivery, callback to a malicious server, and post-exploitation activities, potentially resulting in unauthorized access, lateral movement, and further compromise of the affected systems.
The following analytic identifies alerts triggered by the Office 365 Security and Compliance Center, indicating potential threats or policy violations. It leverages data from the `o365_management_activity` dataset, focusing on events where the workload is SecurityComplianceCenter and the operation is AlertTriggered. This activity is significant as it highlights security and compliance issues within the O365 environment, which are crucial for maintaining organizational security. If confirmed malicious, these alerts could indicate attempts to breach security policies, leading to unauthorized access, data exfiltration, or other malicious activities.
The following analytic identifies potential exploitation of Windows Exchange servers via ProxyShell or ProxyNotShell vulnerabilities, followed by post-exploitation activities such as running nltest, Cobalt Strike, Mimikatz, and adding new users. It leverages data from multiple analytic stories, requiring at least five distinct sources to trigger, thus reducing noise. This activity is significant as it indicates a high likelihood of an active compromise, potentially leading to unauthorized access, privilege escalation, and persistent threats within the environment. If confirmed malicious, attackers could gain control over the Exchange server, exfiltrate data, and maintain long-term access.
The following analytic identifies a spike in the number of AWS Security Hub alerts for an EC2 instance within a 4-hour interval. It leverages AWS Security Hub findings data, calculating the average and standard deviation of alerts to detect anomalies. This activity is significant for a SOC as a sudden increase in alerts may indicate potential security incidents or misconfigurations requiring immediate attention. If confirmed malicious, this could signify an ongoing attack, leading to unauthorized access, data exfiltration, or disruption of services on the affected EC2 instance.
This rule generates alerts based on Google Workspace Alerts Center data and aggregates them by severity. It processes events from 'Google Workspace Alerts' and assigns a risk score based on the severity of the alert (LOW, MEDIUM, HIGH). The rule also extracts various details from the alerts such as event count, event type, product event type, security category details, security category, security investigation tool link, security summary, and alert severity.
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