Splunk Security
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The following analytic detects the creation of a Kubernetes cron job, which is a task scheduled to run automatically at specified intervals. It identifies this activity by monitoring Kubernetes Audit logs for the creation events of cron jobs. This behavior is significant for a SOC as it could allow an attacker to execute malicious tasks repeatedly and automatically, posing a threat to the Kubernetes infrastructure. If confirmed malicious, this activity could lead to persistent attacks, service disruptions, or unauthorized access to sensitive information.
This rule detects a registry modification that allows the 'Consent Admin' to perform operations requiring elevation without user consent or credentials. It specifically monitors changes to the 'ConsentPromptBehaviorAdmin' value within the Windows Policies System registry path to '0x00000000'. This activity is significant as it indicates a potential privilege escalation attempt, which could allow an attacker to execute high-privilege tasks without user approval. If confirmed malicious, this could lead to unauthorized administrative access and control over the compromised machine, posing a severe security risk.
The analytic detects the creation of shadow copies using `vssadmin.exe` or `wmic.exe`. This activity is significant because creating shadow copies can be a precursor to ransomware attacks or data exfiltration, allowing attackers to bypass file locks and access sensitive data. If confirmed malicious, this behavior could enable attackers to maintain persistence, recover deleted files, or prepare for further malicious activities, posing a significant risk to the integrity and confidentiality of the system. Adversaries may create shadow copies to access the NTDS.dit file for credential dumping or to exfiltrate data.
The following analytic detects the use of Certify or Certipy tools to enumerate Active Directory Certificate Services (AD CS) environments. It leverages Endpoint Detection and Response (EDR) data, focusing on specific command-line arguments associated with these tools. This activity is significant because it indicates potential reconnaissance or exploitation attempts targeting AD CS, which could lead to unauthorized access or privilege escalation. If confirmed malicious, attackers could gain insights into the AD CS infrastructure, potentially compromising sensitive certificates and escalating their privileges within the network.
The following analytic detects the execution of rare processes that appear only once across the network within a specified timeframe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant for a SOC as it helps identify potentially malicious activities or unauthorized software, which could indicate a security breach or ongoing attack. If confirmed malicious, such rare processes could lead to data theft, privilege escalation, or complete system compromise, making early detection crucial for minimizing impact.
This analytic detects an unusual number of Kerberos Ticket Granting Ticket (TGT) requests for non-existing users from a single source endpoint. It leverages Windows Event ID 4768 and identifies anomalies using the 3-sigma statistical rule. This behavior is significant as it may indicate an adversary performing a user enumeration attack against Active Directory. If confirmed malicious, the attacker could validate a list of usernames, potentially leading to further attacks such as brute force or credential stuffing, compromising the security of the environment.
This analytic detects access to Windows administrative SMB shares (Admin$, IPC$, C$) using the 'dir' command. It leverages Windows Security Event Logs with EventCode 5140 to identify this activity. This behavior is significant as it is commonly used by tools like PsExec/PaExec for staging binaries before creating and starting services on remote endpoints, a technique often employed by adversaries for lateral movement and remote code execution. If confirmed malicious, this activity could allow attackers to propagate malware, such as IcedID, across the network, leading to widespread infection and potential data breaches.
The following analytic identifies potential DNS data exfiltration using a pre-trained deep learning model. It leverages DNS request data from the Network Resolution datamodel and computes features from past events between the same source and domain. The model generates a probability score (pred_is_exfiltration_proba) indicating the likelihood of data exfiltration. This activity is significant as DNS tunneling can be used by attackers to covertly exfiltrate sensitive data. If confirmed malicious, this could lead to unauthorized data access and potential data breaches, compromising the organization's security posture.
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.
This analytic identifies suspicious use of a session cookie by detecting multiple client values (IP, User Agent, etc.) changing for the same Device Token associated with a specific user. It leverages policy evaluation events from successful authentication logs in Okta. This activity is significant as it may indicate an adversary attempting to reuse a stolen web session cookie, potentially bypassing authentication mechanisms. If confirmed malicious, this could allow unauthorized access to user accounts, leading to data breaches or further exploitation within the environment.
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