Vulnerability Detection via Aggregated Network Primitives
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Solution Overview
Problem
Current network traffic monitoring systems, such as IDS and IPS, are limited in detecting new or unknown attacks as they rely on pre-defined rules for known security threats, failing to identify potential vulnerabilities and future attacks.
Innovation Solution
A vulnerability detection system that uses pattern detection techniques and statistical analysis to aggregate low-level and high-level language primitives, comparing them to meta-language policies to identify suspicious activity, even in the absence of specific threat signatures, and generate security alerts or enforcement actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-defined rules and signatures are used for threat detection, then known security threats can be identified, but new or unknown attacks cannot be detected
Solution Approach 1:
The system performs preliminary analysis by translating data transmissions into primitives and aggregating them before final detection. This preliminary processing enables the system to identify patterns and anomalies that may indicate new attacks before they fully manifest, allowing for proactive detection rather than reactive response to known threats only
Solution Approach 2:
The system changes the detection parameters from fixed signatures to dynamic primitive aggregation. By translating data into primitives and aggregating them according to statistical thresholds and policies, the system adapts to new attack patterns without requiring pre-defined rules, thus improving both reliability for known threats and adaptability for unknown attacks
2Productivity
If individual data transmissions are analyzed in isolation, then simple anomalies can be detected, but complex attack patterns involving multiple unrelated transmissions cannot be identified
Solution Approach 1:
The system merges individual data transmission analyses by aggregating primitives from multiple transmissions. This combination allows the system to detect complex attack patterns that span multiple unrelated transmissions, as the aggregated primitives reveal patterns that would be invisible in isolated analysis
Solution Approach 2:
The system adds a temporal and contextual dimension to detection by aggregating primitives across multiple transmissions. This dimensional expansion enables the detection of complex patterns that emerge over time and across different data streams, transforming simple single-transmission analysis into comprehensive multi-transmission pattern recognition
3Reliability
If statistical analysis and primitive aggregation are performed in real-time, then future attacks can be detected, but processing complexity and computational resources increase
Solution Approach 1:
The system segments the detection process into distinct phases: translation of data transmissions into primitives, aggregation of primitives according to policies, and analysis against thresholds. This segmentation allows complex real-time analysis to be broken down into manageable operations, reducing processing complexity while maintaining the ability to detect future attacks
Solution Approach 2:
The system introduces primitives as an intermediary representation between raw data transmissions and final detection decisions. This intermediary layer simplifies the processing complexity by providing a standardized format for analysis, enabling real-time statistical analysis and future attack detection without overwhelming computational resources
Data Source
AI summary
Methods, systems, and computer-readable media are disclosed for detecting vulnerabilities based on aggregated primitives. A particular method includes receiving a plurality of data transmissions. At least one of the data transmissions includes a protocol anomaly that is not indicative of a security threat. The method includes identifying a plurality of primitives associated with the data transmissions. The primitives are aggregated, and an attack condition is identified based on the aggregated primitives. A security alert is generated based on the identified attack condition.