FCP_FWB_AD-7.4 Fortinet FCP - FortiWeb 7.4 Administrator Free Practice Exam Questions (2025 Updated)

A self-signed certificate is useful when all the devices in your network can be configured to trust it. In this case, if your enterprise's computers trust the internal Active Directory or Certificate Authority (CA) server that signed the certificate, the self-signed certificate can be used internally for HTTPS connections without raising trust issues.

In the SVM plot of potential bot activity, you can see that the sample value (orange) is significantly different from the average value (green) and the maximum value (blue) in most of the metrics. This suggests unusual or abnormal behavior, indicating that the connection might be a bot. Typically, botsexhibit patterns that diverge from normal user activity, such as higher frequencies of certain types of requests, abnormal throughput, or an unusual pattern of HTTP requests (such as requests without referers or excessive TCP connections).

XML Schema: In FortiWeb, XML protection rules allow you to define an XML Schema to validate the structure and content of incoming XML documents. This helps protect against attacks like XML injection by ensuring that only well-formed XML requests are processed.

Request URL: You can define a request URL as part of an XML protection rule to specify the URL pattern for which the rule should apply. This allows you to apply different XML protection rules to different endpoints or resources based on the URL.

Bot ML models analyze multiple connections over time instead of analyzing each connection as a single unit: This is the key distinction. Bot ML models focus on analyzing patterns over a period of time, looking at behavioral patterns across multiple requests or connections from the same source to identify potential bot activity. Unlike traditional anomaly detection or API models that may focus on single connections or individual transactions, bot detection typically examines aggregated behavior to identify patterns indicative of bots, such as high-frequency requests or unusual traffic flows.

You should run the vulnerability scan during a maintenance window: Running a vulnerability scan during a maintenance window minimizes the risk of affecting normal operations. Scans can be resource-intensive and may cause disruptions if run during peak hours or when the system is in use.

You should run the vulnerability scan in a test environment: It is important to run the vulnerability scan in a test environment first to avoid unintended disruptions on the live system. This helps to identify potential issues or false positives without impacting production systems.

The web application is unable to accept any more connections because of network socket exhaustion: A Denial of Service (DoS) attack often floods the web server with an overwhelming number of requests, leading to network socket exhaustion. This can prevent the server from accepting new legitimate connections, effectively disrupting service.

The web application server is unable to accept new client sessions due to memory exhaustion: DoS attacks can consume a significant amount of server memory, causing memory exhaustion. This results in the web application being unable to accept new client sessions or handle requests properly.

In the diagram, FortiWeb is positioned between the client and the server, handling encrypted HTTPS traffic from the client and sending unencrypted HTTP traffic to the server. This indicates that FortiWeb is performing SSL offloading, which means it is decrypting the HTTPS traffic from the client, inspecting it, and then re-encrypting the traffic before forwarding it to the server.

Model building: In this stage, you design and develop the ML model, which involves selecting appropriate algorithms and features to detect bot activity.

Model verification: This is where you test and evaluate the model's performance to ensure it can accurately detect bots without false positives or negatives.

Sample collecting: Gathering relevant data samples (e.g., bot and non-bot traffic) to train the machine learning model is crucial to ensure it can learn from various scenarios.

The Attack ID of 20000010 refers to a brute force login attempt, which typically indicates that the client IP (192.168.1.11) is sending suspicious or malicious traffic to the FortiWeb. FortiWeb detected and blocked this suspicious activity, which is why the page is shown as blocked.