EXPLORE DETECTIONS
GSuite Email Suspicious Attachment
The following analytic detects suspicious attachment file extensions in GSuite emails, potentially indicating a spear-phishing attack. It leverages GSuite Gmail logs to identify emails with attachments having file extensions commonly associated with malware, such as .exe, .bat, and .js. This activity is significant as these file types are often used to deliver malicious payloads, posing a risk of compromising targeted machines. If confirmed malicious, this could lead to unauthorized code execution, data breaches, or further network infiltration.
Gsuite Email Suspicious Subject With Attachment
The following analytic identifies Gsuite emails with suspicious subjects and attachments commonly used in spear phishing attacks. It leverages Gsuite email logs, focusing on specific keywords in the subject line and known malicious file types in attachments. This activity is significant for a SOC as spear phishing is a prevalent method for initial compromise, often leading to further malicious actions. If confirmed malicious, this activity could result in unauthorized access, data exfiltration, or further malware deployment, posing a significant risk to the organization's security.
Gsuite Email With Known Abuse Web Service Link
The following analytic detects emails in Gsuite containing links to known abuse web services such as Pastebin, Telegram, and Discord. It leverages Gsuite Gmail logs to identify emails with these specific domains in their links. This activity is significant because these services are commonly used by attackers to deliver malicious payloads. If confirmed malicious, this could lead to the delivery of malware, phishing attacks, or other harmful activities, potentially compromising sensitive information or systems within the organization.
Gsuite Outbound Email With Attachment To External Domain
The following analytic detects outbound emails with attachments sent from an internal email domain to an external domain. It leverages Gsuite Gmail logs, parsing the source and destination email domains, and flags emails with fewer than 20 outbound instances. This activity is significant as it may indicate potential data exfiltration or insider threats. If confirmed malicious, an attacker could use this method to exfiltrate sensitive information, leading to data breaches and compliance violations.
Gsuite suspicious calendar invite
The following analytic detects suspicious calendar invites sent via GSuite, potentially indicating compromised accounts or malicious internal activity. It leverages GSuite calendar logs, focusing on events where a high volume of invites (over 100) is sent within a 5-minute window. This behavior is significant as it may involve the distribution of malicious links or attachments, posing a security risk. If confirmed malicious, this activity could lead to widespread phishing attacks, unauthorized access, or malware distribution within the organization.
Gsuite Suspicious Shared File Name
The following analytic detects shared files in Google Drive with suspicious filenames commonly used in spear phishing campaigns. It leverages GSuite Drive logs to identify documents with titles that include keywords like "dhl," "ups," "invoice," and "shipment." This activity is significant because such filenames are often used to lure users into opening malicious documents or clicking harmful links. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further compromise of the user's system.
Headless Browser Mockbin or Mocky Request
The following analytic detects headless browser activity accessing mockbin.org or mocky.io. It identifies processes with the "--headless" and "--disable-gpu" command line arguments, along with references to mockbin.org or mocky.io. This behavior is significant as headless browsers are often used for automated tasks, including malicious activities like web scraping or automated attacks. If confirmed malicious, this activity could indicate an attempt to bypass traditional browser security measures, potentially leading to data exfiltration or further exploitation of web applications.
Headless Browser Usage
The following analytic detects the usage of headless browsers within an organization. It identifies processes containing the "--headless" and "--disable-gpu" command line arguments, which are indicative of headless browsing. This detection leverages data from the Endpoint.Processes datamodel to identify such processes. Monitoring headless browser usage is significant as these tools can be exploited by adversaries for malicious activities like web scraping, automated testing, and undetected web interactions. If confirmed malicious, this activity could lead to unauthorized data extraction, automated attacks, or other covert operations on web applications.
Hide User Account From Sign-In Screen
The following analytic detects a suspicious registry modification that hides a user account from the Windows Login screen. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" with a value of "0x00000000". This activity is significant as it may indicate an adversary attempting to create a hidden admin account to avoid detection and maintain persistence on the compromised machine. If confirmed malicious, this could allow the attacker to maintain undetected access and control over the system, posing a severe security risk.
Hiding Files And Directories With Attrib exe
The following analytic detects the use of the Windows binary attrib.exe to hide files or directories by marking them with specific flags. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments that include the "+h" flag. This activity is significant because hiding files can be a tactic used by attackers to conceal malicious files or tools from users and security software. If confirmed malicious, this behavior could allow an attacker to persist in the environment undetected, potentially leading to further compromise or data exfiltration.
High Frequency Copy Of Files In Network Share
The following analytic detects a high frequency of file copying or moving within network shares, which may indicate potential data sabotage or exfiltration attempts. It leverages Windows Security Event Logs (EventCode 5145) to monitor access to specific file types and network shares. This activity is significant as it can reveal insider threats attempting to transfer classified or internal files, potentially leading to data breaches or evidence tampering. If confirmed malicious, this behavior could result in unauthorized data access, data loss, or compromised sensitive information.
High Number of Login Failures from a single source
The following analytic detects multiple failed login attempts in Office365 Azure Active Directory from a single source IP address. It leverages Office365 management activity logs, specifically AzureActiveDirectoryStsLogon records, aggregating these logs in 5-minute intervals to count failed login attempts. This activity is significant as it may indicate brute-force attacks or password spraying, which are critical to monitor. If confirmed malicious, an attacker could gain unauthorized access to Office365 accounts, leading to potential data breaches, lateral movement within the organization, or further malicious activities using the compromised account.
High Process Termination Frequency
The following analytic identifies a high frequency of process termination events on a computer within a short period. It leverages Sysmon EventCode 5 logs to detect instances where 15 or more processes are terminated within a 3-second window. This behavior is significant as it is commonly associated with ransomware attempting to avoid exceptions during file encryption. If confirmed malicious, this activity could indicate an active ransomware attack, potentially leading to widespread file encryption and significant data loss.
High Volume of Bytes Out to Url
The following analytic detects a high volume of outbound web traffic, specifically over 1GB of data sent to a URL within a 2-minute window. It leverages the Web data model to identify significant uploads by analyzing the sum of bytes out. This activity is significant as it may indicate potential data exfiltration by malware or malicious insiders. If confirmed as malicious, this behavior could lead to unauthorized data transfer, resulting in data breaches and loss of sensitive information. Immediate investigation is required to determine the legitimacy of the transfer and mitigate any potential threats.
Hosts receiving high volume of network traffic from email server
The following 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.
HTTP C2 Framework User Agent
This Splunk query analyzes web logs to identify and categorize user agents, detecting various types of c2 frameworks. This activity can signify malicious actors attempting to interact with hosts on the network using known default configurations of command and control tools.
HTTP Duplicated Header
Detects when a request has more than one of the same header. This is commonly used in request smuggling and other web based attacks. HTTP Request Smuggling exploits inconsistencies in how front-end and back-end servers parse HTTP requests by using ambiguous or malformed headers to hide malicious requests within legitimate ones. Attackers leverage duplicate headers, particularly Content-Length and Transfer-Encoding, to cause different servers in the chain to disagree on where one request ends and another begins. RFC7230 states that a sender MUST NOT generate multiple header fields with the same field name in a message unless either the entire field value for that header field is defined as a comma-separated list or the header field is a well-known exception.
HTTP Malware User Agent
This Splunk query analyzes web logs to identify and categorize user agents, detecting various types of malware. This activity can signify possible compromised hosts on the network.
HTTP Possible Request Smuggling
HTTP request smuggling is a technique for interfering with the way a web site processes sequences of HTTP requests that are received from one or more users. Request smuggling vulnerabilities are often critical in nature, allowing an attacker to bypass security controls, gain unauthorized access to sensitive data, and directly compromise other application users. This detection identifies a common request smuggling technique of using both Content-Length and Transfer-Encoding headers to cause a parsing confusion between the frontend and backend.
HTTP PUA User Agent
This Splunk query analyzes web logs to identify and categorize user agents, detecting various types of unwanted applications. This activity can signify possible compromised hosts on the network.
HTTP Rapid POST with Mixed Status Codes
This detection identifies rapid-fire POST request attacks where an attacker sends more than 20 POST requests within a 5-second window, potentially attempting to exploit race conditions or overwhelm request handling. The pattern is particularly suspicious when responses vary in size or status codes, indicating successful exploitation attempts or probing for vulnerable endpoints.
HTTP Request to Reserved Name on IIS Server
Detects attempts to exploit a request smuggling technique against IIS that leverages a Windows quirk where requests for reserved Windows device names such as "/con" trigger an early server response before the request body is received. When combined with a Content-Length desynchronization, this behavior can lead to a parsing confusion between frontend and backend.
HTTP RMM User Agent
This Splunk query analyzes web logs to identify and categorize user agents, detecting various types of Remote Monitoring and Mangement applications. This activity can signify possible compromised hosts on the network.
HTTP Scripting Tool User Agent
This Splunk query analyzes web access logs to identify and categorize non-browser user agents, detecting various types of security tools, scripting languages, automation frameworks, and suspicious patterns. This activity can signify malicious actors attempting to interact with web endpoints in non-standard ways.