Final answer:
Anomaly-based detection models are designed to spot deviations from normal behavior, allowing for the detection of novel threats, while misuse-based (signature-based) models match known patterns or signatures. For a large corporate network, an anomaly-based model would be more effective due to its ability to identify new, sophisticated attacks, despite a higher rate of false positives.
Step-by-step explanation:
The differences between an anomaly-based and a misuse-based detection model are foundational in cybersecurity strategies. Anomaly-based detection models work by establishing a baseline of normal activity and then identifying deviations from this baseline as potential threats. This approach is capable of detecting new, previously unknown attacks but can also lead to a higher false-positive rate. On the other hand, misuse-based (also known as signature-based) detection relies on a database of known attack patterns or signatures to identify threats.
For a corporate network of 10,000 users, I would choose an anomaly-based detection model. The primary reason is its ability to detect zero-day exploits and novel attacks that a misuse-based model might miss due to its limited signature database. In a large corporate environment, the diversity of systems and likelihood of sophisticated attacks would make the anomaly-based approach more advantageous despite its potential for raising false alarms.
That said, a hybrid approach leveraging the strengths of both models would be ideal, where known threats are quickly identified by the misuse-based component and novel threats are detected by the anomaly-based system.