FortiWeb is primarily designed to handle HTTP and HTTPS traffic, protecting web applications from various threats. By default, when operating in reverse proxy mode, FortiWeb does not forward non-HTTP/HTTPS protocols to protected servers. However, administrators can configure FortiWeb to handle non-HTTP/HTTPS traffic differently using the config router setting command. This command allows enabling IP-based forwarding (routing) for non-HTTP/HTTPS traffic. When enabled, FortiWeb can route non-HTTP traffic through itself to the appropriate backend servers. ​

Despite this capability, any non-HTTP/HTTPS traffic that is destined directly for a FortiWeb virtual server IP address is dropped. This means that while FortiWeb can be configured to forward non-HTTP/HTTPStraffic to backend servers, it will not process non-HTTP/HTTPS traffic targeted at its own virtual server IPs. ​

Regarding IPv6 routing, FortiWeb does support IPv6 in various operation modes, including reverse proxy, offline inspection, and transparent inspection. However, enabling IPv6 routing requires specific configurations and is not automatically enabled by default. ​

The quickest detection for an SQL injection attack like the one described ('root'; --) would be through known signatures. FortiWeb utilizes signature-based detection to match incoming traffic against predefined attack patterns. Since SQL injection attacks are commonly known and have specific patterns (such as 'root'; --), known signatures would immediately recognize and flag this type of attack.

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.

In FortiWeb's API protection mechanisms, there are distinctions between the traditional API gateway protection schema and the machine learning (ML) based API protection schema:​

Data Type Support: The API gateway protection schema has the capability to support various data types beyond just strings, allowing for more comprehensive validation and enforcement of API schemas. ​

Schema Adaptability: The ML-based API protection schema is designed to automatically learn and adapt to changes in the API structure without requiring manual intervention from administrators. This dynamic learning process enables FortiWeb to identify and protect against anomalies and potential threats in real-time.

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.

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.

In SAML (Security Assertion Markup Language) deployments, the Identity Provider (IdP) is responsible for storing and managing user authentication credentials, such as usernames and passwords. The IdP authenticates the user and then issues a SAML assertion to the Service Provider (SP), which allows the user to access services without needing to re-enter credentials.