Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
404 detections
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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.
This analytic detects file transfers flagged as malware that occurred over non-standard ports (other than 80 and 443) from Cisco Secure Firewall Threat Defense logs. Adversaries may attempt to bypass protocol-based detection or use alternate ports to blend in with other traffic. This analytic identifies these non-conventional flows and surfaces potential evasion techniques. If confirmed malicious, this indicates potential malware delivery or other nefarious activity.
The analytic detects path traversal command-line execution, often used in malicious documents to execute code via msdt.exe for defense evasion. It leverages Endpoint Detection and Response (EDR) data, focusing on specific patterns in process paths. This activity is significant as it can indicate an attempt to bypass security controls and execute unauthorized code. If confirmed malicious, this behavior could lead to code execution, privilege escalation, or persistence within the environment, potentially allowing attackers to deploy malware or leverage other living-off-the-land binaries (LOLBins).
The following analytic detects `spoolsv.exe` writing a `.dll` file, which is unusual behavior and may indicate exploitation of vulnerabilities like CVE-2021-34527 (PrintNightmare). This detection leverages Sysmon EventID 11 to monitor file creation events in the `\spool\drivers\x64\` directory. This activity is significant because `spoolsv.exe` typically does not write DLL files, and such behavior could signify an ongoing attack. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence on the compromised system.
The following analytic detects a suspicious registry modification aimed at disabling security audit logs by adding a specific registry entry. It leverages data from the Endpoint.Registry data model, focusing on changes to the "Control\\MiniNt" registry path. This activity is significant because it can prevent Windows from logging any events to the Security Log, effectively blinding security monitoring efforts. If confirmed malicious, this technique could allow an attacker to operate undetected, making it difficult to trace their actions and compromising the integrity of security audits.
The analytic identifies blocked destinations within a network that are deemed privacy risks by Zscaler. It leverages web proxy logs, focusing on entries marked as "Privacy Risk." Key data points such as device owner, user, URL category, destination URL, and IP are analyzed. This activity is significant for a SOC as it helps monitor and manage privacy risks, ensuring a secure network environment. If confirmed malicious, this activity could indicate attempts to access or exfiltrate sensitive information, posing a significant threat to data privacy and security.
This analytic detects the use of `mavinject.exe` for DLL injection into running processes. It specifically looks for command-line parameters such as `/INJECTRUNNING` and `/HMODULE`. This activity indicates potential arbitrary code execution, a common tactic for malware deployment and persistence. If confirmed malicious, this could allow attackers to execute unauthorized code, escalate privileges, and maintain persistence within the environment, posing a severe security risk.
This analytic detects the execution of AdvancedRun.exe, a utility that allows users to run programs with different administrative privileges, user accounts, or environment settings. It identifies the process by its name or original file name and flags common command-line arguments often used for configuration or specific execution parameters. Monitoring this activity is crucial as AdvancedRun can be leveraged by attackers for remote code execution, privilege escalation, or maintaining persistence within an environment, similar to tools like PsExec. If used maliciously, it could enable the execution of arbitrary commands or configuration-based automation.
This analytic identifies suspicious DNS TXT records using a pre-trained deep learning model. It leverages DNS response data from the Network Resolution data model, categorizing TXT records into known types via regular expressions. Records that do not match known patterns are flagged as suspicious. This activity is significant as DNS TXT records can be used for data exfiltration or command-and-control communication. If confirmed malicious, attackers could use these records to covertly transfer data or receive instructions, posing a severe threat to network security.
The analytic identifies potential malware activities within a network that are blocked by Zscaler. It leverages web proxy logs to filter for blocked actions associated with malware, aggregating occurrences by user, URL, and threat category. This detection is significant for SOC as it highlights attempts to access malicious content, indicating potential compromise or targeted attacks. If confirmed malicious, this activity could signify an ongoing attempt to infiltrate the network, necessitating immediate investigation to prevent further threats and ensure network integrity.
The following analytic detects the use of the .NET compiler csc.exe for on-the-fly compilation of potentially malicious .NET code. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns associated with csc.exe. This activity is significant because adversaries and malware often use this technique to evade detection by compiling malicious code at runtime. If confirmed malicious, this could allow attackers to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
The following analytic detects the use of Regsvr32.exe to register DLLs from suspicious paths such as AppData, ProgramData, or Windows Temp directories. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant because Regsvr32.exe can be abused to proxy execution of malicious code, bypassing traditional security controls. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
The analytic identifies potential exploit attempts involving command and script interpreters that are blocked by Zscaler. It uses web proxy logs to detect incidents where actions are blocked due to exploit references. The detection compiles statistics by user, threat name, URL, hostname, file class, and filename. This activity is significant as it helps identify and mitigate exploit attempts, which are critical for maintaining security. If confirmed malicious, such activity could lead to unauthorized code execution, privilege escalation, or persistent access within the environment, posing a severe threat to organizational security.
The analytic detects instances of `regsvcs.exe` running without command line arguments. This behavior typically indicates process injection, where another process manipulates `regsvcs.exe`. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, IDs, and command-line executions. This activity is significant as it may signal an attempt to evade detection and execute malicious code. If confirmed malicious, the attacker could achieve code execution, potentially leading to privilege escalation, persistence, or access to sensitive information.
The following analytic detects the execution of "dsquery.exe" with arguments targeting `TrustedDomain` queries directly from the command line. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process names and command-line arguments. This activity is significant as it often indicates domain trust discovery, a common step in lateral movement or privilege escalation by adversaries. If confirmed malicious, this could allow attackers to map domain trusts, potentially leading to further exploitation and unauthorized access to trusted domains.
The analytic identifies potential adware activity blocked by Zscaler. It leverages web proxy logs to detect blocked actions associated with adware threats. Key data points such as device owner, user, URL category, destination URL, and IP are analyzed. This activity is significant as adware can degrade system performance, lead to unwanted advertisements, and potentially expose users to further malicious content. If confirmed malicious, it could indicate an attempt to compromise user systems, necessitating further investigation and remediation to prevent potential data breaches or system exploitation.
The following analytic identifies significant legal liability threats blocked by the Zscaler web proxy. It uses web proxy logs to track destinations, device owners, users, URL categories, and actions associated with legal liability. By leveraging statistics on unique fields, it ensures a precise focus on these threats. This activity is significant for SOC as it helps enforce legal compliance and risk management. If confirmed malicious, it could indicate attempts to access legally sensitive or restricted content, potentially leading to legal repercussions and compliance violations.
The following analytic identifies the download of potentially malicious file types, such as .scr, .dll, .bat, and .lnk, within a network. It leverages web proxy logs from Zscaler, focusing on blocked actions and analyzing fields like deviceowner, user, urlcategory, url, dest, and filename. This activity is significant as these file types are often used to spread malware, posing a threat to network security. If confirmed malicious, this activity could lead to malware execution, data compromise, or further network infiltration.
The following analytic detects the use of the 'dd' command to overwrite files on a Linux system. It leverages data from Linux Auditd telemetry, focusing on process execution logs that include command-line details. This activity is significant because adversaries often use the 'dd' command to destroy or irreversibly overwrite files, disrupting system availability and services. If confirmed malicious, this behavior could lead to data destruction, making recovery difficult and potentially causing significant operational disruptions.
The following analytic identifies attempts to download cryptomining software that are blocked by Zscaler. It leverages web proxy logs to detect blocked actions associated with cryptominer threats, analyzing key data points such as device owner, user, URL category, destination URL, and IP. This activity is significant for a SOC as it helps in early identification and mitigation of cryptomining activities, which can compromise network integrity and resource availability. If confirmed malicious, this activity could lead to unauthorized use of network resources for cryptomining, potentially degrading system performance and increasing operational costs.
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