Editing Ai Cybersecurity (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Cybersecurity AI Detection Approaches
! Approach !! Zero-Day Coverage !! False Positive Rate !! Interpretability
|-
| Signature-based || None || Very low || High (exact match)
|-
| Anomaly detection || High || High || Low
|-
| Supervised ML (known attacks) || Low || Medium || Medium (SHAP)
|-
| Hybrid (signature + anomaly) || Medium || Medium || Medium
|-
| Graph-based (network lateral) || Medium || Low || Medium
|}

'''Failure modes''': Adversarial evasion — attackers craft inputs specifically to fool ML models (adversarial examples in malware). Alert fatigue from high false positive rates causes security teams to ignore true positives. Concept drift as attack patterns evolve continuously. Distribution shift between training (lab) data and production (real network) data. Model inversion attacks that reveal training data about network patterns.
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