Refer to the exhibits.
The DOS attack playbook is configured to create an incident when an event handler generates a denial-of-ser/ice (DoS) attack event.
Why did the DOS attack playbook fail to execute?
Understanding the Playbook and its Components:
The exhibit shows the status of a playbook named 'DOS attack' and its associated tasks.
The playbook is designed to execute a series of tasks upon detecting a DoS attack event.
Analysis of Playbook Tasks:
Attach_Data_To_Incident: Task ID placeholder_8fab0102, status is 'upstream_failed,' meaning it did not execute properly due to a previous task's failure.
Get Events: Task ID placeholder_fa2a573c, status is 'success.'
Create SMTP Enumeration incident: Task ID placeholder_3db75c0a, status is 'failed.'
Reviewing Raw Logs:
The error log shows a ValueError: invalid literal for int() with base 10: '10.200.200.100'.
This error indicates that the task attempted to convert a string (the IP address '10.200.200.100') to an integer, which is not possible.
Identifying the Source of the Error:
The error occurs in the file 'incident_operator.py,' specifically in the execute method.
This suggests that the task 'Create SMTP Enumeration incident' is the one causing the issue because it failed to process the data type correctly.
Conclusion:
The failure of the playbook is due to the 'Create SMTP Enumeration incident' task receiving a string value (an IP address) when it expects an integer value. This mismatch in data types leads to the error.
Fortinet Documentation on Playbook and Task Configuration.
Python error handling documentation for understanding ValueError.
Refer to Exhibit:
You are tasked with reviewing a new FortiAnalyzer deployment in a network with multiple registered logging devices. There is only one FortiAnalyzer in the topology.
Which potential problem do you observe?
Understanding FortiAnalyzer Data Policy and Disk Utilization:
FortiAnalyzer uses data policies to manage log storage, retention, and disk utilization.
The Data Policy section indicates how long logs are kept for analytics and archive purposes.
The Disk Utilization section specifies the allocated disk space and the proportions used for analytics and archive, as well as when alerts should be triggered based on disk usage.
Analyzing the Provided Exhibit:
Keep Logs for Analytics: 60 Days
Keep Logs for Archive: 120 Days
Disk Allocation: 300 GB (with a maximum of 441 GB available)
Analytics: Archive Ratio: 30% : 70%
Alert and Delete When Usage Reaches: 90%
Potential Problems Identification:
Disk Space Allocation: The allocated disk space is 300 GB out of a possible 441 GB, which might not be insufficient if the log volume is high, but it is not the primary concern based on the given data.
Analytics-to-Archive Ratio: The ratio of 30% for analytics and 70% for archive is unconventional. Typically, a higher percentage is allocated for analytics since real-time or recent data analysis is often prioritized. A common configuration might be a 70% analytics and 30% archive ratio. The misconfigured ratio can lead to insufficient space for analytics, causing issues with real-time monitoring and analysis.
Retention Periods: While the retention periods could be seen as lengthy, they are not necessarily indicative of a problem without knowing the specific log volume and compliance requirements. The length of these periods can vary based on organizational needs and legal requirements.
Conclusion:
Based on the analysis, the primary issue observed is the analytics-to-archive ratio being misconfigured. This misconfiguration can significantly impact the effectiveness of the FortiAnalyzer in real-time log analysis, potentially leading to delayed threat detection and response.
Fortinet Documentation on FortiAnalyzer Data Policies and Disk Management.
Best Practices for FortiAnalyzer Log Management and Disk Utilization.
Refer to the exhibits.
You configured a spearphishing event handler and the associated rule. However. FortiAnalyzer did not generate an event.
When you check the FortiAnalyzer log viewer, you confirm that FortiSandbox forwarded the appropriate logs, as shown in the raw log exhibit.
What configuration must you change on FortiAnalyzer in order for FortiAnalyzer to generate an event?
Understanding the Event Handler Configuration:
The event handler is set up to detect specific security incidents, such as spearphishing, based on logs forwarded from other Fortinet products like FortiSandbox.
An event handler includes rules that define the conditions under which an event should be triggered.
Analyzing the Current Configuration:
The current event handler is named 'Spearphishing handler' with a rule titled 'Spearphishing Rule 1'.
The log viewer shows that logs are being forwarded by FortiSandbox but no events are generated by FortiAnalyzer.
Key Components of Event Handling:
Log Type: Determines which type of logs will trigger the event handler.
Data Selector: Specifies the criteria that logs must meet to trigger an event.
Automation Stitch: Optional actions that can be triggered when an event occurs.
Notifications: Defines how alerts are communicated when an event is detected.
Issue Identification:
Since FortiSandbox logs are correctly forwarded but no event is generated, the issue likely lies in the data selector configuration or log type matching.
The data selector must be configured to include logs forwarded by FortiSandbox.
Solution:
B . Configure a FortiSandbox data selector and add it to the event handler:
By configuring a data selector specifically for FortiSandbox logs and adding it to the event handler, FortiAnalyzer can accurately identify and trigger events based on the forwarded logs.
Steps to Implement the Solution:
Step 1: Go to the Event Handler settings in FortiAnalyzer.
Step 2: Add a new data selector that includes criteria matching the logs forwarded by FortiSandbox (e.g., log subtype, malware detection details).
Step 3: Link this data selector to the existing spearphishing event handler.
Step 4: Save the configuration and test to ensure events are now being generated.
Conclusion:
The correct configuration of a FortiSandbox data selector within the event handler ensures that FortiAnalyzer can generate events based on relevant logs.
Fortinet Documentation on Event Handlers and Data Selectors FortiAnalyzer Event Handlers
Fortinet Knowledge Base for Configuring Data Selectors FortiAnalyzer Data Selectors
By configuring a FortiSandbox data selector and adding it to the event handler, FortiAnalyzer will be able to accurately generate events based on the appropriate logs.
Which statement best describes the MITRE ATT&CK framework?
Understanding the MITRE ATT&CK Framework:
The MITRE ATT&CK framework is a comprehensive matrix of tactics and techniques used by adversaries to achieve their objectives.
It is widely used for understanding adversary behavior, improving defense strategies, and conducting security assessments.
Analyzing the Options:
Option A: The framework provides detailed technical descriptions of adversary activities, including specific techniques and subtechniques.
Option B: The framework includes information about mitigations and detections for each technique and subtechnique, providing comprehensive guidance.
Option C: MITRE ATT&CK covers a wide range of attack vectors, including those targeting user endpoints, network devices, and servers.
Option D: Some techniques or subtechniques do indeed fall under multiple tactics, reflecting the complex nature of adversary activities that can serve different objectives.
Conclusion:
The statement that best describes the MITRE ATT&CK framework is that it contains some techniques or subtechniques that fall under more than one tactic.
MITRE ATT&CK Framework Documentation.
Security Best Practices and Threat Intelligence Reports Utilizing MITRE ATT&CK.
Exhibit:
Which observation about this FortiAnalyzer Fabric deployment architecture is true?
Understanding FortiAnalyzer Fabric Deployment:
FortiAnalyzer Fabric deployment involves a hierarchical structure where the Fabric root (supervisor) coordinates with multiple Fabric members (collectors and analyzers).
This setup ensures centralized log collection, analysis, and incident response across geographically distributed locations.
Analyzing the Exhibit:
FAZ1-Supervisor is located at AMER HQ and acts as the Fabric root.
FAZ2-Analyzer is a Fabric member located in EMEA.
FAZ3-Collector and FAZ4-Collector are Fabric members located in EMEA and APAC, respectively.
Evaluating the Options:
Option A: The statement indicates that the AMER HQ SOC team cannot run automation playbooks from the Fabric supervisor. This is true because automation playbooks and certain orchestration tasks typically require local execution capabilities which may not be fully supported on the supervisor node.
Option B: High availability (HA) configuration for the supervisor node is a best practice for redundancy but is not directly inferred from the given architecture.
Option C: The EMEA SOC team having access to historical logs only is not correct since FAZ2-Analyzer provides full analysis capabilities.
Option D: The APAC SOC team has access to FortiView and other reporting functions through FAZ4-Collector, but this is not explicitly detailed in the provided architecture.
Conclusion:
The most accurate observation about this FortiAnalyzer Fabric deployment architecture is that the AMER HQ SOC team cannot run automation playbooks from the Fabric supervisor.
Fortinet Documentation on FortiAnalyzer Fabric Deployment.
Best Practices for FortiAnalyzer and Automation Playbooks.
