Refinement Detection Processor for Emerging Threat Alerts
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Solution Overview
Problem
Conventional computing systems lack effective methods to detect and adapt to novel emerging threats, as defenders often have not prepared defenses against new types of attacks, and existing behavior detection systems can produce false positives and require refinement to accurately identify malicious activities.
Innovation Solution
The system employs behavior detectors that correlate multiple events over time to identify emerging threats, using a refinement detection processor to generate refined alerts by accumulating scores from precursor alerts, distinguishing between known and unknown behaviors, and employing machine learning models to classify events and reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional behavior detection systems are used to detect emerging threats, then detection coverage is improved, but false positive rate increases
Solution Approach 1:
The detection system is divided into two independent detectors: a precursor detector that identifies potential threats with high sensitivity, and a refinement detector that validates these precursors with higher specificity. This segmentation allows each detector to specialize - the precursor detector casts a wide net to catch emerging threats, while the refinement detector filters out false positives through additional analysis, thereby maintaining high detection accuracy while reducing false alarms.
2Difficulty of detecting and measuring
If detection thresholds are lowered to catch novel attacks, then detection sensitivity is improved, but false alarm rate increases
Solution Approach 1:
The refinement detector acts as an intermediary between the precursor detector and the final alert generation. When the precursor detector identifies a potential emerging threat with low confidence (due to lowered thresholds), the refinement detector performs additional analysis to either confirm or reject the threat. This intermediary step allows the system to maintain high detection sensitivity by catching novel attacks that would otherwise be missed, while simultaneously reducing false alarms through the additional validation layer.
3Measurement precision
If multiple detection layers are implemented to reduce false positives, then detection precision is improved, but system complexity increases
Solution Approach 1:
The detection system is divided into two independent detectors: a precursor detector that identifies potential threats with high sensitivity, and a refinement detector that validates these precursors with higher specificity. This segmentation allows each detector to specialize - the precursor detector casts a wide net to catch emerging threats, while the refinement detector filters out false positives through additional analysis, thereby maintaining high detection accuracy while reducing false alarms.
Solution Approach 2:
The precursor detector performs preliminary identification of potential emerging threats before they are fully developed or recognized. By detecting precursor events and patterns early in the attack lifecycle, the system can initiate refinement analysis proactively rather than reactively. This preliminary action allows the refinement detector to focus computational resources on high-priority candidates, improving detection precision while managing system complexity through targeted rather than exhaustive analysis.
Data Source
AI summary
A method includes receiving precursor alerts from a precursor detector that detects events from a processing unit, wherein each precursor alert comprises information of an event from the processing unit, the information of an event from the processing unit, detecting a first event in the precursor alerts indicating undesirable behavior and including a first score that is above a first value, setting a first timer for a first period of time, accumulating a score update with the first score of the first event. Upon the score update reaching or exceeding a first threshold value within the first period of time, generating a refined alert.