Jupyter Notebooks
Powerful threat hunting and investigation with Jupyter notebooks.
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Powerful threat hunting and investigation with Jupyter notebooks.
Last updated
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Using the Scanner Python SDK within Jupyter notebooks, you can perform powerful analysis and generate visualizations for threat hunting and incident response.
Here are some examples of the kinds of threat activity that our users investigate with Jupyter notebooks and our SDK.
Unusual API Activity: Check for unexpected spikes or unusual patterns in API usage that could suggest unauthorized access or actions. This includes activities like unusual login attempts (especially from unfamiliar IP addresses or geographies), changes in IAM user permissions, or unexpected resource launches.
Privilege Escalation Attempts: Teams often look for any evidence of attempts to modify roles or policies to escalate privileges. This includes creating or modifying IAM roles and policies, or attaching policies to existing roles that grant higher privileges than usual.
Resource Deletion or Termination: Monitoring for the deletion of logs, object storage buckets, cloud compute instances, or other resources which could be an attempt to hide unauthorized activity or prepare for an attack by disabling security controls.
Suspicious Cross-Account Activity: Looking for activity where resources are being accessed or manipulated across cloud accounts in an unauthorized or unexpected manner, especially if those accounts are not commonly interlinked.
Access from Unauthorized or Risky Locations: Identifying access attempts from geographic locations that are not recognized as typical for the organization, which might suggest compromised credentials.
Use of Previously Unused or Rare AWS Services: Threat actors might experiment with less commonly used cloud services to explore or exploit resources unnoticed. Monitoring for first-time usage or infrequent service interactions can be crucial.
Failed Security Controls Changes: Investigating any failed attempts to change security groups, network ACLs, or other security-related configurations, as these might be signs of an attacker probing for weaknesses.
Compromised User Credentials: If there's an indication that user credentials have been compromised, users can use CloudTrail logs to trace the actions performed by the compromised accounts. This includes logins, resources accessed, data downloaded, or settings changed. The aim is to determine the extent of the breach and to mitigate further unauthorized access.
Unauthorized Resource Manipulation: In cases where there are unauthorized changes to critical resources (like security groups, EC2 instances, RDS databases), CloudTrail logs help in identifying when the changes were made, who made them, and what exactly was changed. This helps in quickly reverting changes and securing the resources.
Detection of Insider Threats: Incident response may involve investigations into suspicious activities that suggest insider threats. Cloud audit logs can be used to monitor and review actions taken by employees, especially those involving sensitive data or critical configurations.
Analysis of Data Exfiltration Attempts: Cloud audit logs can be analyzed to check for large volumes of data access from services like S3 or unusual access patterns, such as accessing data from IPs that do not belong to the organization, or at odd hours.
Evaluation of Response and Mitigation Actions: Post-incident, it's important to evaluate the effectiveness of the response actions. Cloud audit logs can be used to verify that all unauthorized changes were rolled back, compromised accounts were disabled, and that new security measures were implemented correctly.
