Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
72 detections
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The following analytic identifies the registration of a new Multi-Factor Authentication (MFA) method for an AWS account, as logged through Amazon Security Lake (ASL). It detects this activity by monitoring the `CreateVirtualMFADevice` API operation within ASL logs. This behavior is significant because adversaries who gain unauthorized access to an AWS account may register a new MFA method to maintain persistence. If confirmed malicious, this activity could allow attackers to secure their access, making it harder to detect and remove their presence from the compromised environment.
This analytic aggregates and summarizes alerts from Microsoft Defender for Endpoint. It extracts details such as source, file name, severity, process command line, IP address, registry key, signature, description, and unique ID. The detection is not intended to identify new activity from raw data but rather to leverage existing Microsoft Defender for Endpoint alerts for correlation and risk-based alerting. It filters out alerts with a 'clean' verdict and dynamically maps MITRE techniques and assigns risk scores based on the alert's severity.
The following analytic identifies when a new device is enrolled on an Okta account. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud to detect the creation of new device enrollments. This activity is significant as it may indicate a legitimate user settinging up a new device or an adversary adding a device to maintain unauthorized access. If confirmed malicious, this could lead to potential account takeover, unauthorized access, and persistent control over the compromised Okta account. Monitoring this behavior is crucial for detecting and mitigating unauthorized access attempts.
This correlation rule identifies when a user exceeds a predefined risk threshold based on multiple suspicious Okta activities within a 24-hour period. It aggregates risk events from "Suspicious Okta Activity," "Okta Account Takeover," and "Okta MFA Exhaustion" analytic stories. The detection is significant as it highlights potentially compromised user accounts exhibiting multiple tactics, techniques, and procedures (TTPs). If confirmed malicious, this activity could indicate a serious security breach, allowing attackers to gain unauthorized access, escalate privileges, or persist within the environment.
The following analytic identifies suspicious command-line parameters associated with the use of Impacket's smbexec.py for lateral movement. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns indicative of Impacket tool usage. This activity is significant as both Red Teams and adversaries use Impacket for remote code execution and lateral movement. If confirmed malicious, this activity could allow attackers to execute commands on remote endpoints, potentially leading to unauthorized access, data exfiltration, or further compromise of the network.
The following analytic identifies excessive AccessDenied events within an hour timeframe for IAM users in AWS. It leverages AWS CloudTrail logs to detect multiple failed access attempts from the same source IP and user identity. This activity is significant as it may indicate that an access key has been compromised and is being misused for unauthorized discovery actions. If confirmed malicious, this could allow attackers to gather information about the AWS environment, potentially leading to further exploitation or privilege escalation.
The analytic detects the deletion of critical AWS Security Services configurations, such as CloudWatch alarms, GuardDuty detectors, and Web Application Firewall rules. It leverages Amazon Security Lake logs to identify specific API calls like "DeleteLogStream" and "DeleteDetector." This activity is significant because adversaries often use these actions to disable security monitoring and evade detection. If confirmed malicious, this could allow attackers to operate undetected, leading to potential data breaches, unauthorized access, and prolonged persistence within the AWS environment.
The following analytic detects the creation of AWS Network Access Control Lists (ACLs) with all ports open to a specified CIDR. It leverages AWS CloudTrail events, specifically monitoring for `CreateNetworkAclEntry` or `ReplaceNetworkAclEntry` actions with rules allowing all traffic. This activity is significant because it can expose the network to unauthorized access, increasing the risk of data breaches and other malicious activities. If confirmed malicious, an attacker could exploit this misconfiguration to gain unrestricted access to the network, potentially leading to data exfiltration, service disruption, or further compromise of the AWS environment.
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.
This analytic identifies an AWS IAM account with concurrent sessions originating from more than one unique IP address within a 5-minute span. This detection leverages AWS CloudTrail logs, specifically the `DescribeEventAggregates` API call, to identify multiple IP addresses associated with the same user session. This behavior is significant as it may indicate a session hijacking attack, where an adversary uses stolen session cookies to access AWS resources from a different location. If confirmed malicious, this activity could allow unauthorized access to sensitive corporate resources, leading to potential data breaches or further exploitation.
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 following analytic identifies AWS console login events by users from a new country. It leverages AWS CloudTrail events and compares them against a lookup file of previously seen users and their login locations. This activity is significant because logins from new countries can indicate potential unauthorized access or compromised accounts. If confirmed malicious, this could lead to unauthorized access to AWS resources, data exfiltration, or further exploitation within the AWS environment.
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 following analytic identifies potential AWS S3 exfiltration behavior by correlating multiple risk events related to Collection and Exfiltration techniques. It leverages risk events from AWS sources, focusing on instances where two or more unique analytics and distinct MITRE ATT&CK IDs are triggered for a specific risk object. This activity is significant as it may indicate an ongoing data exfiltration attempt, which is critical for security teams to monitor. If confirmed malicious, this could lead to unauthorized access and theft of sensitive information, compromising the organization's data integrity and confidentiality.
The following analytic detects attempts to disable multi-factor authentication (MFA) for an AWS IAM user. It leverages Amazon Security Lake logs, specifically monitoring for `DeleteVirtualMFADevice` or `DeactivateMFADevice` API operations. This activity is significant as disabling MFA can indicate an adversary attempting to weaken account security to maintain persistence using a compromised account. If confirmed malicious, this action could allow attackers to retain access to the AWS environment without detection, potentially leading to unauthorized access to sensitive resources and prolonged compromise.
The analytic identifies multiple failed multi-factor authentication (MFA) requests to an AWS Console for a single user within a short timeframe (more than 10 failed MFA prompts within 5 minutes). This activity may indicate an adversary attempting to bypass MFA through 'MFA fatigue' attacks, where repeated authentication requests are sent to the user in hopes they will eventually approve one, leading to unauthorized access to the AWS environment.
This analytic identifies potential privilege escalation activities within an organization's Active Directory (AD) environment by correlating multiple analytics from the "Active Directory Privilege Escalation" analytic story within a specified time frame. This is significant for a SOC as it helps identify coordinated attempts to gain elevated privileges, which could indicate a serious security threat. If confirmed malicious, this activity could allow attackers to gain unauthorized access to sensitive systems and data, leading to potential data breaches and further compromise of the network.
The analytic detects cloud provisioning activities originating from previously unseen regions. It leverages cloud infrastructure logs to identify events where resources are started or created, and cross-references these with a baseline of known regions. This activity is significant as it may indicate unauthorized access or misuse of cloud resources from unfamiliar locations. If confirmed malicious, this could lead to unauthorized resource creation, potential data exfiltration, or further compromise of cloud infrastructure.
The following analytic detects cloud provisioning activities originating from previously unseen IP addresses. It leverages cloud infrastructure logs to identify events where resources are created or started, and cross-references these with a baseline of known IP addresses. This activity is significant as it may indicate unauthorized access or potential misuse of cloud resources. If confirmed malicious, an attacker could gain unauthorized control over cloud resources, leading to data breaches, service disruptions, or increased operational costs.
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