Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
734 detections
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This analytic identifies an unusual number of failed authentication attempts to the AWS Console originating from a single source IP address, targeting multiple valid user accounts. It leverages AWS CloudTrail logs and statistical analysis (3-sigma rule) to detect anomalies in failed login attempts. This behavior is indicative of a password spraying attack, where an adversary attempts to gain initial access or elevate privileges by trying a single password against many accounts. Successful exploitation could lead to unauthorized access, data breaches, or further compromise of the AWS environment.
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.
The following analytic detects the deletion of AWS Network Access Control Lists (ACLs). It leverages AWS CloudTrail logs to identify events where a user deletes a network ACL entry. This activity is significant because deleting a network ACL can remove critical access restrictions, potentially allowing unauthorized access to cloud instances. If confirmed malicious, this action could enable attackers to bypass network security controls, leading to unauthorized access, data exfiltration, or further compromise of the cloud environment.
The analytic detects the deletion of AWS CloudTrail logs by identifying `DeleteTrail` events within CloudTrail logs. This detection leverages CloudTrail data to monitor for successful `DeleteTrail` actions, excluding those initiated from the AWS console. This activity is significant because adversaries may delete CloudTrail logs to evade detection and operate stealthily within the compromised environment. If confirmed malicious, this action could allow attackers to cover their tracks, making it difficult to trace their activities and potentially leading to prolonged unauthorized access and further exploitation.
The following analytic detects the creation of AWS KMS keys with an encryption policy accessible to everyone, including external entities. It leverages AWS CloudTrail logs to identify `CreateKey` or `PutKeyPolicy` events where the `kms:Encrypt` action is granted to all principals. This activity is significant as it may indicate a compromised account, allowing an attacker to misuse the encryption key to target other organizations. If confirmed malicious, this could lead to unauthorized data encryption, potentially disrupting operations and compromising sensitive information across multiple entities.
The following analytic detects when AWS S3 bucket versioning is suspended by a user. It leverages AWS CloudTrail logs to identify `PutBucketVersioning` events with the `VersioningConfiguration.Status` set to `Suspended`. This activity is significant because disabling versioning can prevent recovery of deleted or modified data, which is a common tactic in ransomware attacks. If confirmed malicious, this action could lead to data loss and hinder recovery efforts, severely impacting data integrity and availability.
The following analytic detects multiple failed attempts to assume an AWS IAM role, indicating a potential brute force attack. It leverages AWS CloudTrail logs to identify `MalformedPolicyDocumentException` errors with a status of `failure` and filters out legitimate AWS services. This activity is significant as repeated failures to assume roles can indicate an adversary attempting to guess role names, which is a precursor to unauthorized access. If confirmed malicious, this could lead to unauthorized access to AWS resources, potentially compromising sensitive data and services.
The analytic identifies AWS console login events by users from a new city within the last hour. It leverages AWS CloudTrail events and compares them against a lookup file of previously seen user locations. This activity is significant for a SOC as it may indicate unauthorized access or credential compromise, especially if the login originates from an unusual location. If confirmed malicious, this could lead to unauthorized access to AWS resources, data exfiltration, or further exploitation within the cloud environment.
The analytic detects when a user removes an organization from GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for organization deletion events, which could indicate unauthorized removal of critical business resources. For a SOC, identifying organization removals is crucial as it may signal account compromise, insider threats, or malicious attempts to disrupt business operations by deleting entire organizational structures. The impact could be severe, potentially resulting in loss of source code, repositories, team structures, access controls, and other critical organizational assets. This disruption could halt development workflows, cause data loss, and require significant effort to restore from backups if available. Additionally, unauthorized organization removal could be part of a larger attack campaign aimed at destroying or compromising enterprise assets.
This analytic detects an abnormally high number of cloud instances launched within a 4-hour period. It leverages cloud infrastructure logs and applies a probability density model to identify outliers based on historical data. This activity is significant for a SOC because a sudden spike in instance creation could indicate unauthorized access or misuse of cloud resources. If confirmed malicious, this behavior could lead to resource exhaustion, increased costs, or provide attackers with additional compute resources to further their objectives.
The following analytic detects `PutBucketLifecycle` events in AWS CloudTrail logs where a user sets a lifecycle rule for an S3 bucket with an expiration period of fewer than three days. This detection leverages CloudTrail logs to identify suspicious lifecycle configurations. This activity is significant because attackers may use it to delete CloudTrail logs quickly, thereby evading detection and impairing forensic investigations. If confirmed malicious, this could allow attackers to cover their tracks, making it difficult to trace their actions and respond to the breach effectively.
The following analytic identifies when a policy is deleted in AWS. It leverages Amazon Security Lake logs to detect the DeletePolicy API operation. Monitoring policy deletions is crucial as it can indicate unauthorized attempts to weaken security controls. If confirmed malicious, this activity could allow an attacker to remove critical security policies, potentially leading to privilege escalation or unauthorized access to sensitive resources.
The following analytic identifies unusual modifications to security groups in your cloud environment by users, focusing on actions such as modifications, deletions, or creations over 30-minute intervals. It leverages cloud infrastructure logs and calculates the standard deviation for each user, using the 3-sigma rule to detect anomalies. This activity is significant as it may indicate a compromised account or insider threat. If confirmed malicious, attackers could alter security group configurations, potentially exposing sensitive resources or disrupting services.
The following analytic detects cloud provisioning activities originating from previously unseen countries. It leverages cloud infrastructure logs and compares the geographic location of the source IP address against a baseline of known locations. This activity is significant as it may indicate unauthorized access or potential compromise of cloud resources. If confirmed malicious, an attacker could gain control over cloud assets, leading to data breaches, service disruptions, or further infiltration into the network.
The analytic detects suspicious modifications to AWS AMI attributes, such as sharing an AMI with another AWS account or making it publicly accessible. It leverages AWS CloudTrail logs to identify these changes by monitoring specific API calls. This activity is significant because adversaries can exploit these modifications to exfiltrate sensitive data stored in AWS resources. If confirmed malicious, this could lead to unauthorized access and potential data breaches, compromising the confidentiality and integrity of organizational information.
The following analytic identifies a successful Console Login authentication event for an AWS IAM user account without Multi-Factor Authentication (MFA) enabled. It leverages AWS CloudTrail logs to detect instances where MFA was not used during login. This activity is significant as it may indicate a misconfiguration, policy violation, or potential account takeover attempt. If confirmed malicious, an attacker could gain unauthorized access to the AWS environment, potentially leading to data exfiltration, resource manipulation, or further privilege escalation.
The following analytic detects the execution of the `Find-LocalAdminAccess` cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet is part of PowerView, a toolkit for Windows domain enumeration. Identifying the use of `Find-LocalAdminAccess` is crucial as adversaries may use it to find machines where the current user has local administrator access, facilitating lateral movement or privilege escalation. If confirmed malicious, this activity could allow attackers to target and compromise additional systems within the network, significantly increasing their control and access to sensitive information.
The following analytic detects `StopLogging` events within AWS CloudTrail logs, a critical action that adversaries may use to evade detection. By halting the logging of their malicious activities, attackers aim to operate undetected within a compromised AWS environment. This detection is achieved by monitoring for specific CloudTrail log entries that indicate the cessation of logging activities. Identifying such behavior is crucial for a Security Operations Center (SOC), as it signals an attempt to undermine the integrity of logging mechanisms, potentially allowing malicious activities to proceed without observation. The impact of this evasion tactic is significant, as it can severely hamper incident response and forensic investigations by obscuring the attacker's actions.
The analytic detects `PutBucketLifecycle` events in AWS CloudTrail logs where a user sets a lifecycle rule for an S3 bucket with an expiration period of fewer than three days. This activity is significant because attackers may use it to delete CloudTrail logs quickly, thereby evading detection and impairing forensic investigations. If confirmed malicious, this could allow attackers to cover their tracks, making it difficult to trace their actions and respond to the breach effectively.
The 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 following analytic detects the upload of new containers to AWS Elastic Container Service (ECR) outside of standard business hours through AWS CloudTrail events. It identifies this behavior by monitoring for `PutImage` events occurring before 8 AM or after 8 PM, as well as any uploads on weekends. This activity is significant for a SOC to investigate as it may indicate unauthorized access or malicious deployments, potentially leading to compromised services or data breaches. Identifying and addressing such uploads promptly can mitigate the risk of security incidents and their associated impacts.
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