Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This query visualises total inbound emails with Malware detections summarizing the data by the top recipient email address (RecipientEmailAddress)
This query visualises total emails with Phish detections over time summarizing the data daily by Delivery Location.
'This query uses the Azure Defender Security Nested Recommendations data to find machines vulnerable to OMIGOD CVE-2021-38647.
OMI is the Linux equivalent of Windows WMI and helps users manage configurations across remote and local environments. The query aims to find machines that have this OMI vulnerability (CVE-2021-38647).
Security Nested Recommendations data is sent to Microsoft Sentinel using the continuous export feature of Azure Defender(refrence link below).
Reference: https://www.wiz.io/blog/omigod-critical-vulnerabilities-in-omi-azure
Reference: https://docs.microsoft.com/azure/security-center/continuous-export?tabs=azure-portal'
OMI is the Linux equivalent of Windows WMI and helps users manage configurations across remote and local environments. The query aims to find machines that have this OMI vulnerability (CVE-2021-38647).
Security Nested Recommendations data is sent to Microsoft Sentinel using the continuous export feature of Azure Defender(refrence link below).
Reference: https://www.wiz.io/blog/omigod-critical-vulnerabilities-in-omi-azure
Reference: https://docs.microsoft.com/azure/security-center/continuous-export?tabs=azure-portal'
This query visualises bad traffic (% of emails with threats) compared to total inbound emails over time summarising the data daily.
This query visualises the amount of emails subject to an admin policy with action of block, summarizing the data daily
This analytic detects suspicious modifications to Unix shell configuration files, such as `.bashrc` or `.profile`, which may indicate an attempt to alter system behavior or gain unauthorized access. Unauthorized changes to these files can be used to execute malicious commands, escalate privileges, or hide malicious activities. By monitoring for unusual or unauthorized modifications to shell configuration files, this analytic helps identify potential security threats, allowing security teams to respond quickly and mitigate risks.
This query visualises the daily amount of admin false negative submission by submission type.
'This detection highlights executables deployed to hosts via either the Default Domain or Default Domain Controller Policies. These policies apply to all hosts or Domain Controllers and best practice is that these policies should not be used for deployment of files.
A threat actor may use these policies to deploy files or scripts to all hosts in a domain.
This query uses the ASIM parsers and will need them deployed before usage - https://docs.microsoft.com/azure/sentinel/normalization'
A threat actor may use these policies to deploy files or scripts to all hosts in a domain.
This query uses the ASIM parsers and will need them deployed before usage - https://docs.microsoft.com/azure/sentinel/normalization'
This query visualises the total amount of admin false negative or false positive submissions by the verdict of the submission grading.
This query visualises the top admins performing false positive submissions
Identifies suspicious child processes of SolarWinds.Orion.Core.BusinessLayer.dll that may be evidence of the SUNBURST backdoor
References:
- https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html
- https://gist.github.com/olafhartong/71ffdd4cab4b6acd5cbcd1a0691ff82f
To use this analytics rule, make sure you have deployed the [ASIM normalization parsers](https://aka.ms/ASimProcessEvent)'
References:
- https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html
- https://gist.github.com/olafhartong/71ffdd4cab4b6acd5cbcd1a0691ff82f
To use this analytics rule, make sure you have deployed the [ASIM normalization parsers](https://aka.ms/ASimProcessEvent)'
The following analytic detects attempts to stop a service on Linux systems. It leverages data from Linux Auditd. This activity is significant as adversaries often stop or terminate security or critical services to disable defenses or disrupt operations, as seen in malware like Industroyer2. If confirmed malicious, this could lead to the disabling of security mechanisms, allowing attackers to persist, escalate privileges, or deploy destructive payloads, severely impacting system integrity and availability.
The following analytic detects the suspicious auditd service stop. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents.
The analytic detects a spike in the number of API calls made to cloud infrastructure by a user. It leverages cloud infrastructure logs and compares the current API call volume against a baseline probability density function to identify anomalies. This activity is significant because an unusual increase in API calls can indicate potential misuse or compromise of cloud resources. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or disruption of cloud services, posing a significant risk to the organization's cloud environment.
The analytic detects cloud API calls executed by user roles that have not previously run these commands. It leverages the Change data model in Splunk to identify commands executed by users with the user_type of AssumedRole and a status of success. This activity is significant because new commands from different user roles can indicate potential malicious activity or unauthorized actions. If confirmed malicious, this behavior could lead to unauthorized access, data breaches, or other damaging outcomes by exploiting new or unmonitored commands within the cloud environment.
The analytic detects the creation of cloud compute instances using previously unseen image IDs. It leverages cloud infrastructure logs to identify new image IDs that have not been observed before. This activity is significant because it may indicate unauthorized or suspicious activity, such as the deployment of malicious payloads or unauthorized access to sensitive information. If confirmed malicious, this could lead to data breaches, unauthorized access, or further compromise of the cloud environment. Immediate investigation is required to determine the legitimacy of the instance creation and to mitigate potential threats.
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.
The following analytic identifies when a new device is enrolled on an Okta account. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud to detect the creation of new device enrollments. This activity is significant as it may indicate a legitimate user settinging up a new device or an adversary adding a device to maintain unauthorized access. If confirmed malicious, this could lead to potential account takeover, unauthorized access, and persistent control over the compromised Okta account. Monitoring this behavior is crucial for detecting and mitigating unauthorized access attempts.
The following analytic identifies suspicious command-line parameters associated with the use of Impacket's smbexec.py for lateral movement. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns indicative of Impacket tool usage. This activity is significant as both Red Teams and adversaries use Impacket for remote code execution and lateral movement. If confirmed malicious, this activity could allow attackers to execute commands on remote endpoints, potentially leading to unauthorized access, data exfiltration, or further compromise of the network.
The following analytic detects suspicious database file and directory discovery activities, which may signal an attacker attempt to locate and assess critical database assets on a compromised system. This behavior is often a precursor to data theft, unauthorized access, or privilege escalation, as attackers seek to identify valuable information stored in databases. By monitoring for unusual or unauthorized attempts to locate database files and directories, this analytic aids in early detection of potential reconnaissance or data breach efforts, enabling security teams to respond swiftly and mitigate the risk of further compromise.
The analytic identifies AWS console login attempts by users from a new region. It leverages AWS CloudTrail events and compares current login regions against a baseline of previously seen regions for each user. This activity is significant as it may indicate unauthorized access attempts or compromised credentials. If confirmed malicious, an attacker could gain unauthorized access to AWS resources, potentially leading to data breaches, resource manipulation, or further lateral movement within the cloud environment.
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