Singular IDS sensors generate 400 alerts per minute, up to a daily average of 400,000 alerts, without being under duress (the entire system generates millions of alerts daily). The typical system examines incoming traffic for risks and threats based on identified patterns and generates an alert at each instance. But solely depending on network traffic is not enough.
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Contextual data enriches data sources and provides a broader system view. Contextual anomaly detection contributes to better overall anomaly detection via filtration. Filtration reduces false positives, and using metadata to find the source improves attribution. AI modeling of multivariate / time-variant systems uses the deviation of real / expected outputs for contextual anomaly detection purposes.
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#ai security modeling uses #datascience techniques to mine insights and drive #cybersecurity decisions. Alerts are classified using semantic similarity metrics and a nearest-neighbor clustering algorithm. Contrasts in the model and network activity allow the system to detect intruders. i.e. contextual anomaly detection filters anomalous behavior and reduces false positives via metadata to find the source for attribution.
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