Which mechanism does Fortinet utilize to detect advanced persistent threats (APTs)?

Fortinet employs machine learning and behavior analysis as a mechanism to detect advanced persistent threats (APTs) due to the sophisticated nature of these attacks, which often bypass traditional security measures. APTs typically involve a prolonged effort to infiltrate a network, making them challenging to detect using only signature-based methods that rely on known attack patterns or rules.

Machine learning enables Fortinet's systems to analyze vast amounts of network data and identify anomalies indicative of APTs. This analysis is done in real-time, allowing for the detection of malicious behavior that deviates from the established norms of network activity. By engaging in behavior analysis, Fortinet can uncover subtle signs of compromise that may not be associated with known signatures or attack vectors.

In contrast, relying solely on signature-based detection could leave a network vulnerable, as APTs often utilize novel techniques that do not match existing signatures. Firewall rule sets primarily focus on controlling access rather than identifying sophisticated attacks. Lastly, while security patches are essential for protecting systems against known vulnerabilities, they do not actively detect or mitigate ongoing advanced attacks. Hence, using machine learning and behavior analysis is crucial for effectively identifying and countering APTs in a proactive manner.