Network Security Profile Builder for Attack Pattern Detection
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Solution Overview
Problem
Current managed network security systems face challenges in identifying attack patterns and malicious activities amidst normal network telemetry, leading to false positives and reduced confidence in detecting compromise events, as malicious actors modify their tactics to mimic normal traffic.
Innovation Solution
A system comprising a normalizer, profile builder, primitive creator, and compromise detector that structures raw data, builds historical activity profiles, generates primitives from improbable network telemetry, and organizes them into groups to identify specific combinations or sequences indicative of attack patterns or suspicious activities, providing context for clients to investigate and remediate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network security systems monitor and analyze network telemetry to identify malicious activities, then detection capability is provided, but false positives increase and confidence in detection decreases as malicious actors mimic normal traffic
Solution Approach 1:
The system performs preliminary actions by building historical baseline profiles of normal network activity for each client before detecting anomalies. The profile builder component analyzes historical telemetry data to establish what constitutes normal behavior patterns, which are then used as reference points for detecting deviations that may indicate compromise events. This preliminary characterization of normality enables more accurate detection while reducing false positives.
Solution Approach 2:
The system replaces traditional signature-based or rule-based detection mechanisms with a statistical anomaly detection approach. Instead of relying on predefined patterns that malicious actors can evade by mimicking normal traffic, the system uses baseline profiles and statistical analysis to dynamically identify deviations from expected behavior. This substitution enables detection of previously unseen attack patterns while maintaining high confidence in results.
2Measurement precision
If security systems analyze all network telemetry data to identify attack patterns, then comprehensive detection is achieved, but system complexity and computational resources increase
Solution Approach 1:
The system segments the network monitoring function into distinct modular components: a profile builder that creates baseline profiles for each client, a primitive creator that identifies anomalous events, and a compromise detector that correlates primitives to detect compromise events. Each component operates independently on segmented data, analyzing specific aspects of network telemetry rather than processing all data centrally. This modular architecture reduces system complexity while maintaining comprehensive detection capability.
Solution Approach 2:
The system adds a temporal dimension to network monitoring by incorporating historical baseline profiles into the detection process. Instead of analyzing current telemetry in isolation, the system compares current activity against historical patterns across time, enabling detection of anomalies that deviate from established baselines. This temporal dimension enhances detection comprehensiveness without requiring analysis of all possible telemetry dimensions simultaneously.
3Productivity
If security systems use simplified detection methods to reduce false positives, then processing speed increases, but ability to identify sophisticated attack patterns decreases
Solution Approach 1:
The system performs preliminary analysis by building baseline profiles of normal activity patterns during periods when no compromise events are occurring. These pre-computed baselines capture the normal behavior characteristics of each client, including typical traffic volumes, protocols used, and operational patterns. When detecting potential compromise events, the system compares current activity against these pre-established baselines rather than performing comprehensive analysis of all possible attack patterns, enabling fast processing while maintaining sophisticated detection capability.
Solution Approach 2:
The system dynamically adjusts detection parameters based on the established baseline profiles. Instead of using fixed thresholds or rules, the system adapts detection sensitivity to match the specific normal behavior patterns of each client. This allows the system to maintain high processing speed by using simplified comparison operations while achieving sophisticated attack pattern identification through customized, client-specific parameter adjustments.
Data Source
AI summary
Systems and methods for identifying attack patterns or suspicious activity can include a profile builder, a primitive creator, and a compromise detector. The profile builder can populate one or more baseline activity profiles for each client of the plurality of clients or entities associated therewith. The primitive creator can create primitives by comparing identified or extracted features to information in the one or more baseline activity profiles. The compromise detector can receive primitives, and based on identified combinations or sequences of primitives, generate compromise events to be provided to clients.