Which IDS capability is required to satisfy a time-based induction machine mandate and supports detecting anomalies in real time?

Which IDS capability is required to satisfy a time-based induction machine mandate and supports detecting anomalies in real time?

• A. Signature-based anomaly detection
• B. Pattern matching
• C. Real-time anomaly detection
• D. Statistical-based anomaly detection

Answer: (C)

Detecting anomalies as events happen is essential when there is a strict time requirement for detection and response. Real-time anomaly detection is designed to process activity as it streams in, analyze it immediately, and raise alerts without delay. This low-latency capability is what lets an intrusion detection system satisfy time-based mandates, enabling rapid incident response and investigation. Signature-based approaches look for known patterns, which is great for known threats but not for unexpected or evolving behavior. Pattern matching is a broader technique and doesn’t by itself guarantee real-time action. Statistical-based anomaly detection can identify unusual behavior, but it often relies on models and thresholds that may introduce delays or require batch processing. Real-time anomaly detection explicitly prioritizes immediate analysis and alerting, making it the best fit for a time-critical requirement.

Detecting anomalies as events happen is essential when there is a strict time requirement for detection and response. Real-time anomaly detection is designed to process activity as it streams in, analyze it immediately, and raise alerts without delay. This low-latency capability is what lets an intrusion detection system satisfy time-based mandates, enabling rapid incident response and investigation.

Signature-based approaches look for known patterns, which is great for known threats but not for unexpected or evolving behavior. Pattern matching is a broader technique and doesn’t by itself guarantee real-time action. Statistical-based anomaly detection can identify unusual behavior, but it often relies on models and thresholds that may introduce delays or require batch processing. Real-time anomaly detection explicitly prioritizes immediate analysis and alerting, making it the best fit for a time-critical requirement.