FCP_FWB_AD-7.4 Exam Dumps - FCP - FortiWeb 7.4 Administrator

In Fortinet's high availability (HA) configurations, integrating FortiWeb with a traffic distributor like FortiADC is best achieved using the Active-Active HA mode. This mode allows multiple FortiWeb appliances to operate simultaneously, distributing traffic loads and enhancing both performance and redundancy.​

FortiWeb supports several HA modes:​

Active-Passive: One appliance actively handles all traffic, while the other remains on standby, ready to take over if the active unit fails.​

Active-Active: Multiple appliances actively process traffic concurrently, sharing the load and providing redundancy.​

High Volume Active-Active: An enhanced version of Active-Active, designed for environments with exceptionally high traffic volumes.​

When integrating with a traffic distributor like FortiADC, the Active-Active mode is particularly advantageous. FortiADC can intelligently distribute incoming traffic across multiple active FortiWeb appliances, optimizing resource utilization and ensuring high availability. This setup not only balances the load but also provides fault tolerance; if one appliance becomes unavailable, FortiADC can redirect traffic to the remaining active units without service interruption.​

This collaborative approach between FortiWeb and FortiADC ensures that web applications remain secure, performant, and resilient against failures.

The first layer of the FortiWeb anomaly machine learning (ML) analysis mechanism focuses on analyzing traffic and creating a probability model for parameters and HTTP methods to detect potential anomalies. It also assesses traffic patterns over time to determine whether certain behavior is anomalous. These functions are key to understanding and classifying traffic before further analysis is done.

Protected hostname: In reverse proxy mode, the protected hostname refers to the domain or hostname that FortiWeb will protect. It specifies which hostname FortiWeb is acting as a reverse proxy for, and is required for the server policy configuration.

Virtual server: A virtual server is a logical representation of a web server that FortiWeb handles. It's required to configure how traffic is routed to the protected resources in reverse proxy mode.

Cross-Site Scripting (XSS) is a type of web security vulnerability that allows attackers to inject malicious scripts into web pages viewed by users. This can lead to session hijacking, credential theft, or redirection to malicious sites. XSS attacks typically exploit vulnerabilities in web applications that fail to properly sanitize user input.

Here’s an analysis of the given options:

A. SELECT username FROM accounts WHERE username='admin';-- ' AND password='password';

This is an example ofSQL Injection (SQLi)rather than XSS. It manipulates SQL queries to bypass authentication, not execute JavaScript in a user’s browser.

B. <img src="http://badfile/nothere" onerror=alert(document.cookie);>

This is a classicXSS attack.

It uses an tag with a non-existent src attribute.

The onerror event triggers when the image fails to load, executing alert(document.cookie);, which can expose session cookies.

This method is commonly used forstealing cookies or executing arbitrary scripts.

C. SELECT username FROM accounts WHERE username='XSS' ' AND password='alert("http://badurl.com")';

This isneither a valid SQL injection nor a valid XSS attack.

The syntax suggests an incorrect SQL query rather than JavaScript execution in a browser.

D. <IMG SRC="xss.png">

This isnot a valid XSS attackunless there is an additional event handler like onload, onerror, or onmouseover executing JavaScript.

By itself, it just loads an image and does not execute any malicious script.

Thus,Option Bis the correct answer as it represents a real-world XSS attack technique.

References:

OWASP XSS Guide: https://owasp.org/www-community/attacks/xss/

Fortinet XSS Protection Documentation: https://docs.fortinet.com/

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.