Service State Discovery Engine for Network Threat Detection
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Solution Overview
Problem
Existing techniques for identifying unhealthy network activity in communication networks are limited in their ability to detect all evolving threats, necessitating improved methods for continuous monitoring and analysis.
Innovation Solution
The Service State Discovery Engine (SSDE) continuously ingests data related to network application services, aggregates it, and evaluates the state and scale of these services over time to identify changes and anomalies, utilizing components like the Periodicity Engine, Continuity Engine, and Learning Engine to categorize service behavior as periodic, continuous, or mutated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing techniques establish a baseline of healthy network activity and compare ongoing activity to identify threats, then threat identification capability is improved, but the ability to detect all evolving threats deteriorates due to limitations in the baseline comparison approach
Solution Approach 1:
The patent implements dynamic service state discovery that continuously learns and adapts to changing network service behaviors. Instead of using a static baseline, the system dynamically updates its understanding of normal service states through continuous monitoring and machine learning, allowing it to detect both known threats matching the baseline and evolving threats that deviate from learned patterns.
Solution Approach 2:
The system performs self-learning by automatically discovering service states and behaviors without requiring manual baseline configuration. The machine learning components autonomously analyze network traffic patterns, identify normal service behaviors, and adapt to changes, enabling the system to improve its threat detection capability over time without external intervention.
2Measurement precision
If continuous monitoring of network application services is implemented to identify state changes, then detection capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex monitoring task into distinct functional components: service state discovery module, periodicity detection engine, continuity detection engine, and mutation detection engine. Each component handles a specific aspect of analysis, making the overall system more manageable and maintainable while achieving comprehensive detection capability.
Solution Approach 2:
The system introduces service state representations as intermediary data structures that capture essential characteristics of network services. These state representations serve as a bridge between raw network traffic data and threat detection logic, simplifying the analysis process by working with abstracted service states rather than raw packets.
Data Source
AI summary
A computer-implemented system and method, referred to as a Service State Discovery Engine (SSDE), continuously ingests data (such as netflow data) related to a network application service. The SSDE aggregates the ingested data and evaluates the data to identify a state and corresponding nature (e.g., scale) of the network application service. The SSDE identifies changes in the state, and scale of the state, of the network application service over time.