Free Fortinet NSE7_SOC_AR-7.6 Practice Exam with Questions & Answers | Set: 2

Understanding the Requirements:

The objective is to create an incident and generate a report based on malicious attachment events detected by FortiAnalyzer from FortiSandbox analysis.

The endpoint hosts are protected by FortiClient EMS, which is integrated with FortiSandbox. All logs are sent to FortiAnalyzer.

Key Components:

FortiAnalyzer: Centralized logging and analysis for Fortinet devices.

FortiSandbox: Advanced threat protection system that analyzes suspicious files and URLs.

FortiClient EMS: Endpoint management system that integrates with FortiSandbox for endpoint protection.

Playbook Analysis:

The playbook in the exhibit consists of three main actions: GET_EVENTS, RUN_REPORT, and CREATE_INCIDENT.

EVENT_TRIGGER: Starts the playbook when an event occurs.

GET_EVENTS: Fetches relevant events.

RUN_REPORT: Generates a report based on the events.

CREATE_INCIDENT: Creates an incident in the incident management system.

Selecting the Correct Connector:

The correct connector should allow fetching events related to malicious attachments analyzed by FortiSandbox and facilitate integration with FortiAnalyzer.

Connector Options:

FortiSandbox Connector:

Directly integrates with FortiSandbox to fetch analysis results and events related to malicious attachments.

Best suited for getting detailed sandbox analysis results.

Selected as it is directly related to the requirement of handling FortiSandbox analysis events.

FortiClient EMS Connector:

Used for managing endpoint security and integrating with endpoint logs.

Not directly related to fetching sandbox analysis events.

Not selected as it is not directly related to the sandbox analysis events.

FortiMail Connector:

Used for email security and handling email-related logs and events.

Not applicable for sandbox analysis events.

Not selected as it does not relate to the sandbox analysis.

Local Connector:

Handles local events within FortiAnalyzer itself.

Might not be specific enough for fetching detailed sandbox analysis results.

Not selected as it may not provide the required integration with FortiSandbox.

Implementation Steps:

Step 1: Ensure FortiSandbox is configured to send analysis results to FortiAnalyzer.

Step 2: Use the FortiSandbox connector in the playbook to fetch events related to malicious attachments.

Step 3: Configure the GET_EVENTS action to use the FortiSandbox connector.

Step 4: Set up the RUN_REPORT and CREATE_INCIDENT actions based on the fetched events.

Fortinet Documentation on FortiSandbox Integration FortiSandbox Integration Guide

Fortinet Documentation on FortiAnalyzer Event Handling FortiAnalyzer Administration Guide

By using the FortiSandbox connector, the analyst can ensure that the playbook accurately fetches events based on FortiSandbox analysis and generates the required incident and report.

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.

Comprehensive and Detailed Explanation From FortiSOAR 7.6., FortiSIEM 7.3 Exact Extract study guide:

Based on the official documentation forFortiSIEM 7.3(which utilizes the MITRE ATT&CK mapping for incident correlation) andFortiSOAR 7.6(which uses these tactics for incident classification and playbook triggering):

Reconnaissance (Tactic TA0043):This tactic consists of techniques that involve adversaries actively or passively gathering information that can be used to support targeting. In this scenario, the attacker identifies "employee names, roles, and email patterns from public press releases." This is categorized underGather Victim Org Information (T1591)andSearch Open Technical Databases (T1596). Since this activity happens prior to the compromise and involves gathering intelligence, it is strictly Reconnaissance.

Initial Access (Tactic TA0001):This tactic covers techniques that use various entry vectors to gain an initial foothold within a network. The act of sending "tailored emails... to recipients to review an attached agenda using a link" is the definition ofPhishing: Spearphishing Link (T1566.002). This is the specific delivery mechanism used to gain the initial entry.

Why other options are incorrect:

Discovery (B):This tactic involves techniques an adversary uses to gain knowledge about the internal network after they have already gained access. Since the attacker is looking at public press releases, they are operating outside the perimeter.

Defense Evasion (D):This tactic consists of techniques that adversaries use to avoid detection throughout their compromise. While using an external link might bypass some basic reputation filters, the primary goal described in the report is the act of establishing contact and access, which is the core of the Initial Access tactic.

Comprehensive and Detailed Explanation From FortiSOAR 7.6., FortiSIEM 7.3 Exact Extract study guide:

ThePyramid of Pain(David Bianco) is a core concept taught inFortiSIEM 7.3andFortiSOAR 7.6curriculum to help SOC analysts prioritize threat intelligence and detection logic. The model ranks indicators based on the "pain" or effort they cause an adversary to change:

IP Addresses (Easy):These are classified as "Easy" to change. An attacker can simply rotate through a proxy service, use a different VPS, or utilize a new compromised host to continue their campaign. While more valuable than a file hash, they provide relatively low-long term value to the defender because they are so ephemeral.

TTPs (Tough/Hard):This is the apex of the pyramid. TTPs (Tactics, Techniques, and Procedures) represent the fundamental way an adversary operates. If a defender successfully detects and blocks a Tactic (e.g., a specific way an attacker performs privilege escalation), the adversary is forced to reinvent their entire operational process, which is time-consuming and difficult.

Why other options are incorrect:

Artifacts (C):According to the pyramid, Network/Host Artifacts are classified as"Annoying", not "Easy". While an attacker can change them, it requires modifying their code or script behavior, which causes more friction than simply switching an IP address.

Tools (D):Tools are classified as"Challenging". While alternatives exist, an adversary usually invests significant time mastering a specific toolset; losing the ability to use that tool effectively disrupts their efficiency significantly.

Collector2.Worker3.Supervisor4.Agent

The FortiSIEM 7.3 architecture is built upon a distributed multi-tenant model consisting of several distinct functional roles to ensure scalability and performance:

Supervisor:This is the primary management node in a FortiSIEM cluster. It hosts the Graphical User Interface (GUI), the Configuration Management Database (CMDB), and manages the overall system configurations, reporting, and dashboarding.

Worker:These nodes are responsible for the heavy lifting of data processing. They execute real-time event correlation against the rules engine, perform historical search queries, and handle the analytics workload to ensure the Supervisor node is not overwhelmed.

Collector:Collectors are typically deployed at remote sites or different network segments to offload log collection from the central cluster. They receive logs via Syslog, SNMP, or WMI, compress the data, and securely forward it to the Workers or Supervisor. They also perform performance monitoring of local devices.

Agent:These are lightweight software components installed directly on endpoints (Windows/Linux). Their primary role is to collect local endpoint logs, monitor file integrity (system changes), and track user activity that cannot be captured via traditional network-based logging.

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 theanalytics-to-archive ratiobeing misconfigured. This misconfiguration can significantly impact the effectiveness of the FortiAnalyzer in real-time log analysis, potentially leading to delayed threat detection and response.

Comprehensive and Detailed Explanation From FortiSOAR 7.6., FortiSIEM 7.3 Exact Extract study guide:

The built-in Jinja editor inFortiSOAR 7.6is a powerful utility designed to help playbook developers write and test complex data manipulation logic without having to execute the entire playbook. Its primary capabilities include:

Renders output (A):The editor provides a "Preview" or "Evaluation" pane. By combining aJinja expressionwith a sampleJSON input(manually entered or loaded), the editor dynamically calculates and displays the resulting output. This allows for immediate verification of data transformation logic.

Checks validity (B):The editor includes built-in linting and syntax validation. It alerts the developer to errors such as unclosed brackets, incorrect filter usage, or invalid syntax, ensuring that only valid Jinja code is saved into the playbook step.

Loads environment JSON (D):One of the most significant features for troubleshooting is the ability toload the environment JSONfrom a recent execution. This populates the editor's variable context (vars) with the actual data from a specific playbook run, allowing the developer to test expressions against real-world data that recently passed through the system.

Why other options are incorrect:

Creates new records in bulk (C):While Jinja expressions are used to format the data that goes into a record, the actual creation of records is handled by the"Create Record"step or specificConnectors, not by the Jinja editor utility itself.

Defines conditions to trigger a playbook step (E):Jinja is thelanguageused to write conditions within a "Decision" step or "Step Utilities," but the Jinja Editor is a tool forevaluating and testingthose expressions. The definition of the condition logic and the triggering behavior is a function of the Playbook Engine and Step configuration, not the editor's standalone capabilities.