Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
404 detections
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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 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.
The following analytic detects the suspicious sysmon 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.
Detects HTTP requests containing the URI pattern '/asp.asp?ui=' which is known to be used by the Chafer malware for command and control communication to receive data from its C2 server.
Detects the download of Ursnif malware based on specific URL patterns and a successful HTTP status code. The rule looks for URIs containing a forward slash, '.php?l=', and ending with '.cab', combined with an HTTP status code of 200, indicating a successful download.
Detects Ursnif C2 traffic by identifying specific URL patterns in proxy logs. The rule looks for URIs containing base64-like encoding indicators ('_2f', '_2b') in combination with specific file extensions and directory names ('.avi' and '/images/'). This combination is indicative of Ursnif malware's command and control communication.
Detects network communication patterns associated with the Turla ComRAT malware, specifically looking for HTTP/HTTPS requests containing '/index/index.php?h=' in the URI. This pattern is indicative of ComRAT's command and control (C2) communication.
Detects update checks performed by Advanced IP Scanner and Advanced Port Scanner utilities. These utilities are often used for network reconnaissance and can be considered Potentially Unwanted Applications (PUA) in some environments. The rule specifically looks for requests to '/checkupdate.php' with a set of specific query parameters, indicating an update check from these tools.
This rule detects attempts to execute files from external WebDAV shares. It specifically looks for HTTP GET requests where the User-Agent header starts with 'Microsoft-WebDAV-MiniRedir/' and the requested URI ends with common executable or script file extensions. This behavior is often observed in initial access campaigns where adversaries leverage WebDAV to deliver and execute malicious payloads.
This rule detects HTTP web access to domains that exhibit low prevalence within the monitored environment. It specifically filters out internal RFC 1918 IP addresses and ensures that only valid Fully Qualified Domain Names (FQDNs) are considered. The prevalence is determined by a rolling maximum count of unique days a domain has been observed, with alerts triggering for domains seen 3 or fewer times. The risk score is incremented based on how low the prevalence is, with the lowest prevalence (1 day) resulting in the highest risk score.
Executable downloaded by Microsoft Excel by user with GCP entity relationship
This analytic detects Lumma Stealer download attempts using Cisco Secure Firewall Intrusion Events. It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signatures with specific IDs have been triggered. If confirmed malicious, this behavior could indicate an active infection of Lumma Stealer.
Detects potential exploitation attempt of CVE-2023-1389, an Unauthenticated Command Injection vulnerability in TP-Link Archer AX21 routers. The rule looks for specific URI patterns and keywords indicative of an attempt to inject commands via the 'country' parameter.
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 analytic identifies blocked scam-related activities detected by Zscaler within a network. It leverages web proxy logs to examine actions flagged as scam threats, focusing on data points such as device owner, user, URL category, destination URL, and IP. This detection is significant for SOC as it helps in the early identification and mitigation of scam activities, ensuring network safety. If confirmed malicious, this activity could indicate attempts to deceive users, potentially leading to data theft or financial loss.
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.
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 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 identifies excessive AccessDenied events within an hour timeframe for IAM users in AWS. It leverages AWS CloudTrail logs to detect multiple failed access attempts from the same source IP and user identity. This activity is significant as it may indicate that an access key has been compromised and is being misused for unauthorized discovery actions. If confirmed malicious, this could allow attackers to gather information about the AWS environment, potentially leading to further exploitation or privilege escalation.
The analytic identifies web activity related to employment searches within a network using Zscaler web proxy logs. It focuses on entries categorized as 'Job/Employment Search' and analyzes data points such as device owner, user, URL category, destination URL, and IP. This detection helps monitor potential insider threats by identifying users who may be seeking new employment, which could indicate a risk of data exfiltration or other insider threats, potentially leading to sensitive information leakage or other security breaches.
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