Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
2,154 detections
Filters
Last updated
All Time
Detection languages
683
379
342
230
173
Contributors
630
378
354
145
119
Categories
841
831
746
393
348
Platforms
1,587
586
494
263
118
Products / Services
10,670
9,758
6,693
4,421
3,749
2,154
MITRE Techniques
655
561
383
337
286
CVEs
32
20
8
7
6
IDS Classtypes
65
32
23
19
14
IDS Protocols
47
38
30
30
12
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 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 analytic identifies excessive DNS query failures by counting DNS responses that do not indicate success, triggering when there are more than 50 occurrences. It leverages the Network_Resolution data model, focusing on DNS reply codes that signify errors. This activity is significant because a high number of DNS failures can indicate potential network misconfigurations, DNS poisoning attempts, or malware communication issues. If confirmed malicious, this activity could lead to disrupted network services, hindered communication, or data exfiltration attempts by attackers.
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.
The following analytic identifies high inbound or outbound network I/O anomalies in Kubernetes containers. It leverages process metrics from an OTEL collector and Kubelet Stats Receiver, along with data from Splunk Observability Cloud. A lookup table with average and standard deviation values for network I/O is used to detect anomalies persisting over a 1-hour period. This activity is significant as it may indicate data exfiltration, command and control communication, or unauthorized data transfers. If confirmed malicious, it could lead to data breaches, service outages, financial losses, and reputational damage.
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.
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 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 analytic detects spikes in Server Message Block (SMB) traffic connections, which are used for sharing files and resources between computers. It leverages network traffic logs to monitor connections on ports 139 and 445, and SMB application usage. By calculating the average and standard deviation of SMB connections over the past 70 minutes, it identifies sources exceeding two standard deviations from the average. This activity is significant as it may indicate potential SMB-based attacks, such as ransomware or data theft. If confirmed malicious, attackers could exfiltrate data or spread malware within the network.
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.
This analytic identifies a large volume of DNS ANY queries, which may indicate a DNS amplification attack. It leverages the Network_Resolution data model to count DNS queries of type "ANY" directed to specific destinations. This activity is significant because DNS amplification attacks can overwhelm network resources, leading to Denial of Service (DoS) conditions. If confirmed malicious, this activity could disrupt services, degrade network performance, and potentially be part of a larger Distributed Denial of Service (DDoS) attack, impacting the availability of critical infrastructure.
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 a significant increase in data transfers from an email server to client hosts. It uses statistical analysis on network traffic to detect anomalies based on average and standard deviation of outbound bytes. This activity is significant as it may indicate a malicious actor exfiltrating data via the email server, potentially leading to unauthorized data access and breaches.
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 unauthorized devices attempting to connect to the organization's network by inspecting DHCP request packets. It detects this activity by comparing the MAC addresses in DHCP requests against a list of known authorized devices stored in the assets_by_str.csv file. This activity is significant for a SOC because unauthorized devices can pose security risks, including potential data breaches or network disruptions. If confirmed malicious, this activity could allow an attacker to gain unauthorized network access, potentially leading to further exploitation or data exfiltration.
The analytic identifies hosts receiving an unusually high volume of network traffic from an email server. It leverages the Network_Traffic data model to sum incoming bytes to clients from email servers, comparing current traffic against historical averages and standard deviations. This activity is significant as it may indicate data exfiltration by a malicious actor using the email server. If confirmed malicious, this could lead to unauthorized data access and potential data breaches, compromising sensitive information and impacting organizational security.
The analytic identifies spikes in the number of Server Message Block (SMB) connections using the Machine Learning Toolkit (MLTK). It leverages the Network_Traffic data model to monitor SMB traffic on ports 139 and 445, applying a machine learning model to detect anomalies. Sudden increases in SMB traffic can indicate lateral movement or data exfiltration attempts by attackers. If confirmed malicious, this behavior could lead to unauthorized access, data theft, or further compromise of the network.
The following analytic detects the presence of punycode within x509 certificates using Zeek x509 logs. It identifies punycode in the subject alternative name email and other fields by searching for the "xn--" prefix. This activity is significant as punycode can be used in phishing attacks or to bypass domain filters, posing a security risk. If confirmed malicious, attackers could use these certificates to impersonate legitimate domains, potentially leading to unauthorized access or data breaches.
Page 97 of 103