FCSS_SOC_AN-7.4: FCSS - Security Operations 7.4 Analyst

Understanding the Threat Hunting Data:

The Threat Hunting Monitor in the provided exhibits shows various application services, their usage counts, and data metrics such as sent bytes, average sent bytes, and maximum sent bytes.

The second part of the exhibit lists connection attempts from a specific source IP (10.0.1.10) to a destination IP (8.8.8.8), with repeated 'Connection Failed' messages.

Analyzing the Application Services:

DNS is the top application service with a significantly high count (251,400) and notable sent bytes (9.1 MB).

This large volume of DNS traffic is unusual for regular DNS queries and can indicate the presence of DNS tunneling.

DNS Tunneling:

DNS tunneling is a technique used by attackers to bypass security controls by encoding data within DNS queries and responses. This allows them to extract data from the local network without detection.

The high volume of DNS traffic, combined with the detailed metrics, suggests that DNS tunneling might be in use.

Connection Failures to 8.8.8.8:

The repeated connection attempts from the source IP (10.0.1.10) to the destination IP (8.8.8.8) with connection failures can indicate an attempt to communicate with an external server.

Google DNS (8.8.8.8) is often used for DNS tunneling due to its reliability and global reach.

Conclusion:

Given the significant DNS traffic and the nature of the connection attempts, it is reasonable to conclude that DNS tunneling is being used to extract confidential data from the local network.

Why Other Options are Less Likely:

Spearphishing (A): There is no evidence from the provided data that points to spearphishing attempts, such as email logs or phishing indicators.

Reconnaissance (C): The data does not indicate typical reconnaissance activities, such as scanning or probing mail servers.

FTP C&C (D): There is no evidence of FTP traffic or command-and-control communications using FTP in the provided data.

SANS Institute: 'DNS Tunneling: How to Detect Data Exfiltration and Tunneling Through DNS Queries' SANS DNS Tunneling

OWASP: 'DNS Tunneling' OWASP DNS Tunneling

By analyzing the provided threat hunting data, it is evident that DNS tunneling is being used to exfiltrate data, indicating a sophisticated method of extracting confidential information from the network.

Understanding the FortiAnalyzer Fabric:

The FortiAnalyzer Fabric provides centralized log collection, analysis, and reporting for connected FortiGate devices.

Devices in a FortiAnalyzer Fabric can be organized into different Administrative Domains (ADOMs) to separate logs and management.

Analyzing the Exhibit:

FAZ-SiteA and FAZ-SiteB are FortiAnalyzer devices in the fabric.

FortiGate-B1 and FortiGate-B2 are shown under the Site-B-Fabric, indicating they are part of the same Security Fabric.

FAZ-SiteA has multiple entries under it: SiteA and MSSP-Local, suggesting multiple ADOMs are enabled.

Evaluating the Options:

Option A: FortiGate-B1 and FortiGate-B2 are under Site-B-Fabric, indicating they are indeed part of the same Security Fabric.

Option B: The presence of FAZ-SiteA and FAZ-SiteB as FortiAnalyzers does not preclude the existence of collectors. However, there is no explicit mention of a separate collector role in the exhibit.

Option C: Not all FortiGate devices are directly registered to the supervisor. The exhibit shows hierarchical organization under different sites and ADOMs.

Option D: The multiple entries under FAZ-SiteA (SiteA and MSSP-Local) indicate that FAZ-SiteA has two ADOMs enabled.

Conclusion:

FortiGate-B1 and FortiGate-B2 are in a Security Fabric.

FAZ-SiteA has two ADOMs enabled.

Fortinet Documentation on FortiAnalyzer Fabric Topology and ADOM Configuration.

Best Practices for Security Fabric Deployment with FortiAnalyzer.

Understanding the Playbook Configuration:

The 'Malicious File Detect' playbook is designed to create an incident when a malicious file detection event is triggered.

The playbook includes tasks such as Attach_Data_To_Incident, Create Incident, and Get Events.

Analyzing the Playbook Execution:

The exhibit shows that the Create Incident task has failed, and the Attach_Data_To_Incident task has also failed.

The Get Events task succeeded, indicating that it was able to retrieve event data.

Reviewing Raw Logs:

The raw logs indicate an error related to parsing input in the incident_operator.py file.

The error traceback suggests that the task was expecting a specific input format (likely a name or number) but received an incorrect data format.

Identifying the Source of the Failure:

The Create Incident task failure is the root cause since it did not proceed correctly due to incorrect input format.

The Attach_Data_To_Incident task subsequently failed because it depends on the successful creation of an incident.

Conclusion:

The primary reason for the playbook execution failure is that the Create Incident task received an incorrect data format, which was not a name or number as expected.

Fortinet Documentation on Playbook and Task Configuration.

Error handling and debugging practices in playbook execution.

Understanding the MITRE ATT&CK Matrix:

The MITRE ATT&CK framework is a knowledge base of adversary tactics and techniques based on real-world observations.

Each tactic in the matrix represents the 'why' of an attack technique, while each technique represents 'how' an adversary achieves a tactic.

Analyzing the Provided Exhibit:

The exhibit shows part of the MITRE ATT&CK Enterprise matrix as displayed on FortiAnalyzer.

The focus is on technique T1071 (Application Layer Protocol), which has subtechniques labeled T1071.001, T1071.002, T1071.003, and T1071.004.

Each subtechnique specifies a different type of application layer protocol used for Command and Control (C2):

T1071.001 Web Protocols

T1071.002 File Transfer Protocols

T1071.003 Mail Protocols

T1071.004 DNS

Identifying Key Points:

Subtechniques under T1071: There are four subtechniques listed under the primary technique T1071, confirming that statement B is true.

Event Handlers for T1071: FortiAnalyzer includes event handlers for monitoring various tactics and techniques. The presence of event handlers for tactic T1071 suggests active monitoring and alerting for these specific subtechniques, confirming that statement C is true.

Misconceptions Clarified:

Statement A (four techniques under tactic T1071) is incorrect because T1071 is a single technique with four subtechniques.

Statement D (15 events associated with the tactic) is misleading. The number 15 refers to the techniques under the Application Layer Protocol, not directly related to the number of events.

Conclusion:

The accurate interpretation of the exhibit confirms that there are four subtechniques under technique T1071 and that there are event handlers covering tactic T1071.

MITRE ATT&CK Framework documentation.

FortiAnalyzer Event Handling and MITRE ATT&CK Integration guides.

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.

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.