Application Data Attack Detection Using Attestable Contexts
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Solution Overview
Problem
Existing cybersecurity systems struggle to detect unknown attack vectors and deviations from programmed behavior in application data flows, particularly for sensitive data, as vulnerabilities without known CVEs or attack vectors remain undetected.
Innovation Solution
A system and method that captures, catalogs, and encodes attestable contexts when application services interact with sensitive data, using telemetry to analyze spans, generate contexts, and integrate them into a continuous integration pipeline, enabling deterministic detection of unclassified patterns and preemptive mechanisms to flag and investigate unexpected behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cybersecurity systems use known CVE lists and programmed behavior patterns for detection, then detection accuracy for known threats is improved, but the ability to detect unknown attack vectors and zero-day vulnerabilities deteriorates
Solution Approach 1:
The system performs preliminary action by capturing and cataloging attestable contexts before attacks occur. Telemetry data is collected and encoded into contextual patterns during normal operation, creating a baseline of legitimate behavior that enables detection of deviations without relying on pre-known vulnerability signatures.
Solution Approach 2:
The system implements feedback mechanisms where detected deviations from contextual patterns trigger investigations, and investigation results feed back into refining the contextual catalogs. This continuous learning loop improves the system's ability to detect both known and unknown threats by adapting to new attack patterns while maintaining accuracy for known vulnerabilities.
2Adaptability or versatility
If the system captures and catalogs attestable contexts for all application services, then detection capability for unknown attack vectors is improved, but system complexity and computational overhead increase
Solution Approach 1:
The system segments the monitoring task by dividing application services into discrete contexts with specific data flows. Each context is captured and cataloged independently, allowing the system to manage complexity through modular organization of telemetry data while maintaining comprehensive detection coverage across all services.
Solution Approach 2:
The patent introduces an intermediary layer that translates complex telemetry data into encoded contextual patterns. This intermediary processing stage simplifies the raw data by extracting and cataloging only the essential attestable contexts, reducing computational overhead while preserving detection capability.
3Speed
If the system analyzes all telemetry data in real-time, then detection speed for attack vectors is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary encoding of telemetry data into contextual patterns during normal operation, storing these catalogs for rapid comparison. During detection, the system only needs to compare incoming telemetry against the pre-encoded contexts, significantly reducing real-time computational requirements while maintaining fast detection speed.
Solution Approach 2:
The system applies partial action by focusing computational resources only on analyzing deviations from established contextual patterns rather than processing all telemetry data in full detail. This selective analysis approach reduces computational overhead while maintaining detection effectiveness for attack vectors.
Data Source
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AI summary
In one embodiment, a method for detecting an unknown attack vector, by a system, includes receiving a marked span that has been flagged for inspection. The method further includes conducting a root cause analysis to determine if the marked span should be classified as an attack. In response to a determination that the marked span should be classified as an attack, the method further includes determining whether the marked span engaged with data corresponding to one or more application services defining the marked span. The method further includes designating the data corresponding to the one or more application services as compromised in response to a determination that the marked span did engage with said data.