Update v8.14.2
editUpdate v8.14.2edit
This section lists all updates associated with version 8.14.2 of the Fleet integration Prebuilt Security Detection Rules.
| Rule | Description | Status | Version |
|---|---|---|---|
Identifies an expiration lifecycle configuration added to an S3 bucket. Lifecycle configurations can be used to manage objects in a bucket, including setting expiration policies. This rule detects when a lifecycle configuration is added to an S3 bucket, which could indicate that objects in the bucket will be automatically deleted after a specified period of time. This could be used to evade detection by deleting objects that contain evidence of malicious activity. |
new |
1 |
|
Identifies when a specified inbound (ingress) rule is added or adjusted for a VPC security group in AWS EC2. This rule detects when a security group rule is added that allows traffic from any IP address or from a specific IP address to common remote access ports, such as 22 (SSH) or 3389 (RDP). Adversaries may add these rules to allow remote access to VPC instances from any location, increasing the attack surface and potentially exposing the instances to unauthorized access. |
new |
1 |
|
Identifies the first occurrence of an AWS resource establishing a session via SSM to an EC2 instance. Adversaries may use AWS Systems Manager to establish a session to an EC2 instance to execute commands on the instance. This can be used to gain access to the instance and perform actions such as privilege escalation. This rule helps detect the first occurrence of this activity for a given AWS resource. |
new |
1 |
|
Systemctl is a process used in Linux systems to manage systemd processes through service configuration files. Malicious actors can leverage systemd services to achieve persistence by creating or modifying service files to execute malicious commands or payloads during system startup. This allows them to maintain unauthorized access, execute additional malicious activities, or evade detection. |
new |
1 |
|
The Secure Shell (SSH) authorized_keys file specifies which users are allowed to log into a server using public key authentication. Adversaries may modify it to maintain persistence on a victim host by adding their own public key(s). |
update |
206 |
|
Identifies the first occurrence of a user identity in AWS using |
update |
3 |
|
First Time Seen AWS Secret Value Accessed in Secrets Manager |
An adversary with access to a compromised AWS service such as an EC2 instance, Lambda function, or other service may attempt to leverage the compromised service to access secrets in AWS Secrets Manager. This rule looks for the first time a specific user identity has programmatically retrieved a specific secret value from Secrets Manager using the |
update |
311 |
Identifies when a Route53 Resolver Query Log Configuration is deleted. When a Route53 Resolver query log configuration is deleted, Resolver stops logging DNS queries and responses for the specified configuration. Adversaries may delete query log configurations to evade detection or cover their tracks. |
update |
2 |
|
Identifies an AWS Amazon Machine Image (AMI) being shared with another AWS account. Adversaries with access may share an AMI with an external AWS account as a means of data exfiltration. AMIs can contain secrets, bash histories, code artifacts, and other sensitive data that adversaries may abuse if shared with unauthorized accounts. AMIs can be made publicly available accidentally as well. |
update |
2 |
|
A statistical model has identified command-and-control (C2) beaconing activity. Beaconing can help attackers maintain stealthy communication with their C2 servers, receive instructions and payloads, exfiltrate data and maintain persistence in a network. |
update |
5 |
|
Statistical Model Detected C2 Beaconing Activity with High Confidence |
A statistical model has identified command-and-control (C2) beaconing activity with high confidence. Beaconing can help attackers maintain stealthy communication with their C2 servers, receive instructions and payloads, exfiltrate data and maintain persistence in a network. |
update |
5 |
This rule detects the creation or modification of an authorized_keys or sshd_config file inside a container. The Secure Shell (SSH) authorized_keys file specifies which users are allowed to log into a server using public key authentication. Adversaries may modify it to maintain persistence on a victim host by adding their own public key(s). Unexpected and unauthorized SSH usage inside a container can be an indicator of compromise and should be investigated. |
update |
3 |
|
A machine learning job has detected data exfiltration to a particular geo-location (by region name). Data transfers to geo-locations that are outside the normal traffic patterns of an organization could indicate exfiltration over command and control channels. |
update |
4 |
|
Potential Data Exfiltration Activity to an Unusual IP Address |
A machine learning job has detected data exfiltration to a particular geo-location (by IP address). Data transfers to geo-locations that are outside the normal traffic patterns of an organization could indicate exfiltration over command and control channels. |
update |
4 |
Potential Data Exfiltration Activity to an Unusual Destination Port |
A machine learning job has detected data exfiltration to a particular destination port. Data transfer patterns that are outside the normal traffic patterns of an organization could indicate exfiltration over command and control channels. |
update |
4 |
A machine learning job has detected data exfiltration to a particular geo-location (by region name). Data transfers to geo-locations that are outside the normal traffic patterns of an organization could indicate exfiltration over command and control channels. |
update |
4 |
|
A machine learning job has detected high bytes of data written to an external device. In a typical operational setting, there is usually a predictable pattern or a certain range of data that is written to external devices. An unusually large amount of data being written is anomalous and can signal illicit data copying or transfer activities. |
update |
4 |
|
A machine learning job has detected high bytes of data written to an external device via Airdrop. In a typical operational setting, there is usually a predictable pattern or a certain range of data that is written to external devices. An unusually large amount of data being written is anomalous and can signal illicit data copying or transfer activities. |
update |
4 |
|
A machine learning job has detected a rare process writing data to an external device. Malicious actors often use benign-looking processes to mask their data exfiltration activities. The discovery of such a process that has no legitimate reason to write data to external devices can indicate exfiltration. |
update |
4 |
|
Machine Learning Detected DGA activity using a known SUNBURST DNS domain |
A supervised machine learning model has identified a DNS question name that used by the SUNBURST malware and is predicted to be the result of a Domain Generation Algorithm. |
update |
4 |
A population analysis machine learning job detected potential DGA (domain generation algorithm) activity. Such activity is often used by malware command and control (C2) channels. This machine learning job looks for a source IP address making DNS requests that have an aggregate high probability of being DGA activity. |
update |
4 |
|
Machine Learning Detected a DNS Request With a High DGA Probability Score |
A supervised machine learning model has identified a DNS question name with a high probability of sourcing from a Domain Generation Algorithm (DGA), which could indicate command and control network activity. |
update |
4 |
Machine Learning Detected a DNS Request Predicted to be a DGA Domain |
A supervised machine learning model has identified a DNS question name that is predicted to be the result of a Domain Generation Algorithm (DGA), which could indicate command and control network activity. |
update |
4 |
This rule detects setting modifications for protected branches of a GitHub repository. Branch protection rules can be used to enforce certain workflows or requirements before a contributor can push changes to a branch in your repository. Changes to these protected branch settings should be investigated and verified as legitimate activity. Unauthorized changes could be used to lower your organization’s security posture and leave you exposed for future attacks. |
update |
3 |
|
A machine learning job has detected unusually high number of process arguments in an RDP session. Executing sophisticated attacks such as lateral movement can involve the use of complex commands, obfuscation mechanisms, redirection and piping, which in turn increases the number of arguments in a command. |
update |
4 |
|
A machine learning job has detected unusually high mean of RDP session duration. Long RDP sessions can be used to evade detection mechanisms via session persistence, and might be used to perform tasks such as lateral movement, that might require uninterrupted access to a compromised machine. |
update |
4 |
|
A machine learning job has detected an unusually high file size shared by a remote host indicating potential lateral movement activity. One of the primary goals of attackers after gaining access to a network is to locate and exfiltrate valuable information. Instead of multiple small transfers that can raise alarms, attackers might choose to bundle data into a single large file transfer. |
update |
4 |
|
A machine learning job has detected unusually high variance of RDP session duration. Long RDP sessions can be used to evade detection mechanisms via session persistence, and might be used to perform tasks such as lateral movement, that might require uninterrupted access to a compromised machine. |
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4 |
|
An anomaly detection job has detected a remote file transfer on an unusual directory indicating a potential lateral movement activity on the host. Many Security solutions monitor well-known directories for suspicious activities, so attackers might use less common directories to bypass monitoring. |
update |
4 |
|
An anomaly detection job has detected a remote file transfer with a rare extension, which could indicate potential lateral movement activity on the host. |
update |
4 |
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A machine learning job has detected a high count of destination IPs establishing an RDP connection with a single source IP. Once an attacker has gained access to one system, they might attempt to access more in the network in search of valuable assets, data, or further access points. |
update |
4 |
|
A machine learning job has detected a high count of source IPs establishing an RDP connection with a single destination IP. Attackers might use multiple compromised systems to attack a target to ensure redundancy in case a source IP gets detected and blocked. |
update |
4 |
|
A machine learning job has detected unusually high number of processes started in a single RDP session. Executing a large number of processes remotely on other machines can be an indicator of lateral movement activity. |
update |
4 |
|
A machine learning job has detected an abnormal volume of remote files shared on the host indicating potential lateral movement activity. One of the primary goals of attackers after gaining access to a network is to locate and exfiltrate valuable information. Attackers might perform multiple small transfers to match normal egress activity in the network, to evade detection. |
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4 |
|
A machine learning job has detected an RDP session started at an usual time or weekday. An RDP session at an unusual time could be followed by other suspicious activities, so catching this is a good first step in detecting a larger attack. |
update |
4 |
|
Identifies when a Microsoft 365 Mailbox is accessed by a ClientAppId that was observed for the fist time during the last 10 days. |
update |
106 |
|
A machine learning job has detected a suspicious Windows process. This process has been classified as suspicious in two ways. It was predicted to be suspicious by the ProblemChild supervised ML model, and it was found to be an unusual process, on a host that does not commonly manifest malicious activity. Such a process may be an instance of suspicious or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules. |
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5 |
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A machine learning job has detected a suspicious Windows process. This process has been classified as malicious in two ways. It was predicted to be malicious by the ProblemChild supervised ML model, and it was found to be an unusual child process name, for the parent process, by an unsupervised ML model. Such a process may be an instance of suspicious or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules. |
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5 |
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A machine learning job has detected a suspicious Windows process. This process has been classified as malicious in two ways. It was predicted to be malicious by the ProblemChild supervised ML model, and it was found to be suspicious given that its user context is unusual and does not commonly manifest malicious activity,by an unsupervised ML model. Such a process may be an instance of suspicious or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules. |
update |
5 |
|
Machine Learning Detected a Suspicious Windows Event Predicted to be Malicious Activity |
A supervised machine learning model (ProblemChild) has identified a suspicious Windows process event with high probability of it being malicious activity. Alternatively, the model’s blocklist identified the event as being malicious. |
update |
5 |
Machine Learning Detected a Suspicious Windows Event with a High Malicious Probability Score |
A supervised machine learning model (ProblemChild) has identified a suspicious Windows process event with high probability of it being malicious activity. Alternatively, the model’s blocklist identified the event as being malicious. |
update |
5 |
A machine learning job combination has detected a set of one or more suspicious Windows processes with unusually high scores for malicious probability. These process(es) have been classified as malicious in several ways. The process(es) were predicted to be malicious by the ProblemChild supervised ML model. If the anomaly contains a cluster of suspicious processes, each process has the same host name, and the aggregate score of the event cluster was calculated to be unusually high by an unsupervised ML model. Such a cluster often contains suspicious or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules. |
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5 |
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Suspicious Windows Process Cluster Spawned by a Parent Process |
A machine learning job combination has detected a set of one or more suspicious Windows processes with unusually high scores for malicious probability. These process(es) have been classified as malicious in several ways. The process(es) were predicted to be malicious by the ProblemChild supervised ML model. If the anomaly contains a cluster of suspicious processes, each process has the same parent process name, and the aggregate score of the event cluster was calculated to be unusually high by an unsupervised ML model. Such a cluster often contains suspicious or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules. |
update |
5 |
A machine learning job combination has detected a set of one or more suspicious Windows processes with unusually high scores for malicious probability. These process(es) have been classified as malicious in several ways. The process(es) were predicted to be malicious by the ProblemChild supervised ML model. If the anomaly contains a cluster of suspicious processes, each process has the same user name, and the aggregate score of the event cluster was calculated to be unusually high by an unsupervised ML model. Such a cluster often contains suspicious or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules. |
update |
5 |
|
This rule monitors for network connections from a kworker process. kworker, or kernel worker, processes are part of the kernel’s workqueue mechanism. They are responsible for executing work that has been scheduled to be done in kernel space, which might include tasks like handling interrupts, background activities, and other kernel-related tasks. Attackers may attempt to evade detection by masquerading as a kernel worker process. |
update |
5 |
|
Suspicious Network Activity to the Internet by Previously Unknown Executable |
This rule monitors for network connectivity to the internet from a previously unknown executable located in a suspicious directory. An alert from this rule can indicate the presence of potentially malicious activity, such as the execution of unauthorized or suspicious processes attempting to establish connections to unknown or suspicious destinations such as a command and control server. Detecting and investigating such behavior can help identify and mitigate potential security threats, protecting the system and its data from potential compromise. |
update |
9 |
This rule monitors for the copying or moving of a system binary to a suspicious directory. Adversaries may copy/move and rename system binaries to evade detection. Copying a system binary to a different location should not occur often, so if it does, the activity should be investigated. |
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8 |
|
Monitors for the deletion of the kernel ring buffer events through dmesg. Attackers may clear kernel ring buffer events to evade detection after installing a Linux kernel module (LKM). |
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5 |
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Monitors for kernel processes with associated process executable fields that are not empty. Unix kernel processes such as kthreadd and kworker typically do not have process.executable fields associated to them. Attackers may attempt to hide their malicious programs by masquerading as legitimate kernel processes. |
update |
3 |
|
This rule monitors for the usage of the which command with an unusual amount of process arguments. Attackers may leverage the which command to enumerate the system for useful installed utilities that may be used after compromising a system to escalate privileges or move latteraly across the network. |
update |
6 |
|
This detection rule identifies suspicious network traffic patterns associated with TCP reverse shell activity. This activity consists of a network event that is followed by the creation of a shell process with suspicious command line arguments. An attacker may establish a Linux TCP reverse shell to gain remote access to a target system. |
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3 |
|
This detection rule identifies suspicious network traffic patterns associated with TCP reverse shell activity. This activity consists of a parent-child relationship where a network event is followed by the creation of a shell process. An attacker may establish a Linux TCP reverse shell to gain remote access to a target system. |
update |
9 |
|
Detects the use of the chkconfig binary to manually add a service for management by chkconfig. Threat actors may utilize this technique to maintain persistence on a system. When a new service is added, chkconfig ensures that the service has either a start or a kill entry in every runlevel and when the system is rebooted the service file added will run providing long-term persistence. |
update |
112 |
|
Linux cron jobs are scheduled tasks that can be leveraged by malicious actors for persistence, privilege escalation and command execution. By creating or modifying cron job configurations, attackers can execute malicious commands or scripts at predefined intervals, ensuring their continued presence and enabling unauthorized activities. |
update |
10 |
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Detects the copying of the Linux dynamic loader binary and subsequent file creation for the purpose of creating a backup copy. This technique was seen recently being utilized by Linux malware prior to patching the dynamic loader in order to inject and preload a malicious shared object file. This activity should never occur and if it does then it should be considered highly suspicious or malicious. |
update |
109 |
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Detects the manual creation of files in specific etc directories, via user root, used by Linux malware to persist and elevate privileges on compromised systems. File creation in these directories should not be entirely common and could indicate a malicious binary or script installing persistence mechanisms for long term access. |
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114 |
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Files that are placed in the /etc/init.d/ directory in Unix can be used to start custom applications, services, scripts or commands during start-up. Init.d has been mostly replaced in favor of Systemd. However, the "systemd-sysv-generator" can convert init.d files to service unit files that run at boot. Adversaries may add or alter files located in the /etc/init.d/ directory to execute malicious code upon boot in order to gain persistence on the system. |
update |
10 |
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Persistence via KDE AutoStart Script or Desktop File Modification |
Identifies the creation or modification of a K Desktop Environment (KDE) AutoStart script or desktop file that will execute upon each user logon. Adversaries may abuse this method for persistence. |
update |
111 |
This rule monitors for a file creation event originating from a kworker parent process. kworker, or kernel worker, processes are part of the kernel’s workqueue mechanism. They are responsible for executing work that has been scheduled to be done in kernel space, which might include tasks like handling interrupts, background activities, and other kernel-related tasks. Attackers may attempt to evade detection by masquerading as a kernel worker process. |
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4 |
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Identifies the attempt to create a new backdoor user by setting the user’s UID to 0. Attackers may alter a user’s UID to 0 to establish persistence on a system. |
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7 |
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Message of the day (MOTD) is the message that is presented to the user when a user connects to a Linux server via SSH or a serial connection. Linux systems contain several default MOTD files located in the "/etc/update-motd.d/" and "/usr/lib/update-notifier/" directories. These scripts run as the root user every time a user connects over SSH or a serial connection. Adversaries may create malicious MOTD files that grant them persistence onto the target every time a user connects to the system by executing a backdoor script or command. This rule detects the creation of potentially malicious files within the default MOTD file directories. |
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10 |
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Message of the day (MOTD) is the message that is presented to the user when a user connects to a Linux server via SSH or a serial connection. Linux systems contain several default MOTD files located in the "/etc/update-motd.d/" and "/usr/lib/update-notifier/" directories. These scripts run as the root user every time a user connects over SSH or a serial connection. Adversaries may create malicious MOTD files that grant them persistence onto the target every time a user connects to the system by executing a backdoor script or command. This rule detects the execution of potentially malicious processes through the MOTD utility. |
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9 |
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This rule monitors the creation/alteration of the rc.local file by a previously unknown process executable through the use of the new terms rule type. The /etc/rc.local file is used to start custom applications, services, scripts or commands during start-up. The rc.local file has mostly been replaced by Systemd. However, through the "systemd-rc-local-generator", rc.local files can be converted to services that run at boot. Adversaries may alter rc.local to execute malicious code at start-up, and gain persistence onto the system. |
update |
111 |
|
Shared Object Created or Changed by Previously Unknown Process |
This rule monitors the creation of shared object files by previously unknown processes. The creation of a shared object file involves compiling code into a dynamically linked library that can be loaded by other programs at runtime. While this process is typically used for legitimate purposes, malicious actors can leverage shared object files to execute unauthorized code, inject malicious functionality into legitimate processes, or bypass security controls. This allows malware to persist on the system, evade detection, and potentially compromise the integrity and confidentiality of the affected system and its data. |
update |
8 |
This rule monitors for the potential edit of a suspicious file. In Linux, when editing a file through an editor, a temporary .swp file is created. By monitoring for the creation of this .swp file, we can detect potential file edits of suspicious files. The execution of this rule is not a clear sign of the file being edited, as just opening the file through an editor will trigger this event. Attackers may alter any of the files added in this rule to establish persistence, escalate privileges or perform reconnaisance on the system. |
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5 |
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Detects the creation of a systemd timer within any of the default systemd timer directories. Systemd timers can be used by an attacker to gain persistence, by scheduling the execution of a command or script. Similarly to cron/at, systemd timers can be set up to execute on boot time, or on a specific point in time, which allows attackers to regain access in case the connection to the infected asset was lost. |
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10 |
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Systemd service files are configuration files in Linux systems used to define and manage system services. Malicious actors can leverage systemd service files to achieve persistence by creating or modifying service files to execute malicious commands or payloads during system startup. This allows them to maintain unauthorized access, execute additional malicious activities, or evade detection. |
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10 |
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Detects potential buffer overflow attacks by querying the "Segfault Detected" pre-built rule signal index, through a threshold rule, with a minimum number of 100 segfault alerts in a short timespan. A large amount of segfaults in a short time interval could indicate application exploitation attempts. |
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3 |
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Identifies the use of osascript to execute scripts via standard input that may prompt a user with a rogue dialog for credentials. |
update |
208 |
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Identifies the access or file open of web browser sensitive files by an untrusted/unsigned process or osascript. Adversaries may acquire credentials from web browsers by reading files specific to the target browser. |
update |
208 |
|
Detects deletion of the quarantine attribute by an unusual process (xattr). In macOS, when applications or programs are downloaded from the internet, there is a quarantine flag set on the file. This attribute is read by Apple’s Gatekeeper defense program at execution time. An adversary may disable this attribute to evade defenses. |
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109 |
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Identifies suspicious child processes of frequently targeted Microsoft Office applications (Word, PowerPoint, and Excel). These child processes are often launched during exploitation of Office applications or by documents with malicious macros. |
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207 |
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Identifies rare processes that do not usually run on individual hosts, which can indicate execution of unauthorized services, malware, or persistence mechanisms. Processes are considered rare when they only run occasionally as compared with other processes running on the host. |
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108 |
|
This rule identifies a potential network sweep. A network sweep is a method used by attackers to scan a target network, identifying active hosts, open ports, and available services to gather information on vulnerabilities and weaknesses. This reconnaissance helps them plan subsequent attacks and exploit potential entry points for unauthorized access, data theft, or other malicious activities. This rule proposes threshold logic to check for connection attempts from one source host to 10 or more destination hosts on commonly used network services. |
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6 |
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Identifies the use of the Exchange PowerShell cmdlet, New-MailBoxExportRequest, to export the contents of a primary mailbox or archive to a .pst file. Adversaries may target user email to collect sensitive information. |
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212 |
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Identifies use of WinRar or 7z to create an encrypted files. Adversaries will often compress and encrypt data in preparation for exfiltration. |
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112 |
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Identifies the creation of a new port forwarding rule. An adversary may abuse this technique to bypass network segmentation restrictions. |
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211 |
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Identifies potential use of an SSH utility to establish RDP over a reverse SSH Tunnel. This can be used by attackers to enable routing of network packets that would otherwise not reach their intended destination. |
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211 |
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Identifies suspicious processes being spawned by the ScreenConnect client processes. This activity may indicate execution abusing unauthorized access to the ScreenConnect remote access software. |
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102 |
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Identifies the creation or modification of Domain Backup private keys. Adversaries may extract the Data Protection API (DPAPI) domain backup key from a Domain Controller (DC) to be able to decrypt any domain user master key file. |
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209 |
|
Identifies the creation of .kirbi files. The creation of this kind of file is an indicator of an attacker running Kerberos ticket dump utilities, such as Mimikatz, and precedes attacks such as Pass-The-Ticket (PTT), which allows the attacker to impersonate users using Kerberos tickets. |
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106 |
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Identifies the password log file from the default Mimikatz memssp module. |
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210 |
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Adversaries can add the hidden attribute to files to hide them from the user in an attempt to evade detection. |
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112 |
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Identifies the creation of the Antimalware Scan Interface (AMSI) DLL in an unusual location. This may indicate an attempt to bypass AMSI by loading a rogue AMSI module instead of the legit one. |
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110 |
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Identifies modifications of the AmsiEnable registry key to 0, which disables the Antimalware Scan Interface (AMSI). An adversary can modify this key to disable AMSI protections. |
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111 |
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Identifies attempts to disable/modify the code signing policy through system native utilities. Code signing provides authenticity on a program, and grants the user with the ability to check whether the program has been tampered with. By allowing the execution of unsigned or self-signed code, threat actors can craft and execute malicious code. |
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8 |
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Identifies attempts to disable the code signing policy through the registry. Code signing provides authenticity on a program, and grants the user with the ability to check whether the program has been tampered with. By allowing the execution of unsigned or self-signed code, threat actors can craft and execute malicious code. |
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10 |
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Identifies modifications to the Windows Defender registry settings to disable the service or set the service to be started manually. |
update |
112 |
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Identifies attempts to disable PowerShell Script Block Logging via registry modification. Attackers may disable this logging to conceal their activities in the host and evade detection. |
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110 |
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Identifies when a user enables DNS-over-HTTPS. This can be used to hide internet activity or the process of exfiltrating data. With this enabled, an organization will lose visibility into data such as query type, response, and originating IP, which are used to determine bad actors. |
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110 |
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Identifies registry write modifications to hide an encoded portable executable. This could be indicative of adversary defense evasion by avoiding the storing of malicious content directly on disk. |
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208 |
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A suspicious Endpoint Security parent process was detected. This may indicate a process hollowing or other form of code injection. |
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112 |
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A suspicious WerFault child process was detected, which may indicate an attempt to run via the SilentProcessExit registry key manipulation. Verify process details such as command line, network connections and file writes. |
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213 |
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Identifies when one or more features on Microsoft Defender are disabled. Adversaries may disable or tamper with Microsoft Defender features to evade detection and conceal malicious behavior. |
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111 |
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Identifies network activity from unexpected system applications. This may indicate adversarial activity as these applications are often leveraged by adversaries to execute code and evade detection. |
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112 |
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Identifies registry modification to the LocalAccountTokenFilterPolicy policy. If this value exists (which doesn’t by default) and is set to 1, then remote connections from all local members of Administrators are granted full high-integrity tokens during negotiation. |
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109 |
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Identifies a SolarWinds binary modifying the start type of a service to be disabled. An adversary may abuse this technique to manipulate relevant security services. |
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110 |
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A suspicious Zoom child process was detected, which may indicate an attempt to run unnoticed. Verify process details such as command line, network connections, file writes and associated file signature details as well. |
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211 |
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Identifies attempt to load an untrusted driver. Adversaries may modify code signing policies to enable execution of unsigned or self-signed code. |
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9 |
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This rule detects the Active Directory query tool, AdFind.exe. AdFind has legitimate purposes, but it is frequently leveraged by threat actors to perform post-exploitation Active Directory reconnaissance. The AdFind tool has been observed in Trickbot, Ryuk, Maze, and FIN6 campaigns. For Winlogbeat, this rule requires Sysmon. |
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112 |
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Identifies the use of dsquery.exe for domain trust discovery purposes. Adversaries may use this command-line utility to enumerate trust relationships that may be used for Lateral Movement opportunities in Windows multi-domain forest environments. |
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8 |
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Identifies the use of nltest.exe for domain trust discovery purposes. Adversaries may use this command-line utility to enumerate domain trusts and gain insight into trust relationships, as well as the state of Domain Controller (DC) replication in a Microsoft Windows NT Domain. |
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111 |
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Identifies a suspicious parent child process relationship with cmd.exe descending from an unusual process. |
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211 |
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Identifies the execution of commonly abused Windows utilities via a delayed Ping execution. This behavior is often observed during malware installation and is consistent with an attacker attempting to evade detection. |
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3 |
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Identifies native Windows host and network enumeration commands spawned by the Windows Management Instrumentation Provider Service (WMIPrvSE). |
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112 |
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Identifies the deletion of backup files, saved using third-party software, by a process outside of the backup suite. Adversaries may delete Backup files to ensure that recovery from a ransomware attack is less likely. |
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113 |
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Identifies use of the wbadmin.exe to delete the backup catalog. Ransomware and other malware may do this to prevent system recovery. |
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112 |
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Potential Ransomware Behavior - High count of Readme files by System |
This rule identifies a high number (20) of file creation event by the System virtual process from the same host and with same file name containing keywords similar to ransomware note files and all within a short time period. |
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2 |
Identifies an incoming SMB connection followed by a suspicious file rename operation. This may indicate a remote ransomware attack via the SMB protocol. |
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2 |
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Identifies an incoming SMB connection followed by the creation of a file with a name similar to ransomware note files. This may indicate a remote ransomware attack via the SMB protocol. |
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2 |
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Identifies use of the built-in Windows script interpreters (cscript.exe or wscript.exe) being used to execute a process via Windows Management Instrumentation (WMI). This may be indicative of malicious activity. |
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110 |
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Identifies suspicious child processes of Microsoft Outlook. These child processes are often associated with spear phishing activity. |
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212 |
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Detects writing executable files that will be automatically launched by Adobe on launch. |
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212 |
|
Detects attempts to maintain persistence by creating registry keys using AppCert DLLs. AppCert DLLs are loaded by every process using the common API functions to create processes. |
update |
209 |
|
A job can be used to schedule programs or scripts to be executed at a specified date and time. Adversaries may abuse task scheduling functionality to facilitate initial or recurring execution of malicious code. |
update |
209 |
|
Detects changes to registry persistence keys that are not commonly used or modified by legitimate programs. This could be an indication of an adversary’s attempt to persist in a stealthy manner. |
update |
108 |
|
Identifies an attempt to reset a potentially privileged account password remotely. Adversaries may manipulate account passwords to maintain access or evade password duration policies and preserve compromised credentials. |
update |
115 |
|
Identifies the creation of a new Windows service with suspicious Service command values. Windows services typically run as SYSTEM and can be used for privilege escalation and persistence. |
update |
9 |
|
Windows services typically run as SYSTEM and can be used as a privilege escalation opportunity. Malware or penetration testers may run a shell as a service to gain SYSTEM permissions. |
update |
211 |
|
An adversary can use the Background Intelligent Transfer Service (BITS) SetNotifyCmdLine method to execute a program that runs after a job finishes transferring data or after a job enters a specified state in order to persist on a system. |
update |
208 |
|
Web Shell Detection: Script Process Child of Common Web Processes |
Identifies suspicious commands executed via a web server, which may suggest a vulnerability and remote shell access. |
update |
212 |
User Account Control (UAC) can help mitigate the impact of malware on Windows hosts. With UAC, apps and tasks always run in the security context of a non-administrator account, unless an administrator specifically authorizes administrator-level access to the system. This rule identifies registry value changes to bypass User Access Control (UAC) protection. |
update |
111 |
|
Identifies the load of a driver with an original file name and signature values that were observed for the first time during the last 30 days. This rule type can help baseline drivers installation within your environment. |
update |
8 |
|
Identifies Service Control (sc.exe) spawning from script interpreter processes to create, modify, or start services. This can potentially indicate an attempt to elevate privileges or maintain persistence. |
update |
109 |
|
Identifies an attempt to bypass User Account Control (UAC) by masquerading as a Microsoft trusted Windows directory. Attackers may bypass UAC to stealthily execute code with elevated permissions. |
update |
113 |
|
Identifies attempts to bypass User Account Control (UAC) by hijacking the Microsoft Management Console (MMC) Windows Firewall snap-in. Attackers bypass UAC to stealthily execute code with elevated permissions. |
update |
112 |
|
Identifies the creation of a Windows service by an unusual client process. Services may be created with administrator privileges but are executed under SYSTEM privileges, so an adversary may also use a service to escalate privileges from administrator to SYSTEM. |
update |
109 |