Network State Machine Modeling for Security Signature Generation
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Solution Overview
Problem
Existing network security systems fail to leverage network configuration and policy data to detect malicious activity effectively, relying solely on traffic observation and anomaly detection.
Innovation Solution
A software representation is generated based on network configuration and policy data, converting the network into a state machine to identify state-to-state transitions, which are used to create detection signatures for monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to detect malicious network activity based solely on traffic observation and anomaly detection, then the system can operate without requiring network configuration and policy data, but the detection precision and ability to understand legitimate network behavior are insufficient
Solution Approach 1:
The system performs preliminary action by generating a software representation of the network using configuration and policy data before actual security monitoring begins. This pre-built model establishes the baseline of legitimate network behavior, enabling more accurate detection of malicious activities without losing valuable configuration information.
Solution Approach 2:
The system creates a software copy or representation of the network that mirrors its configuration and policy structure. This virtual model allows the system to analyze and detect threats based on the copied network blueprint, preserving all configuration information while enabling sophisticated detection capabilities.
2Measurement precision
If network configuration and policy data are integrated into the security system to improve detection capabilities, then the detection precision and understanding of legitimate behavior improve, but the device complexity and implementation difficulty increase
Solution Approach 1:
The system introduces a software representation as an intermediary layer between the network configuration data and the detection engine. This intermediary model simplifies the integration process by providing a standardized interface that translates complex configuration and policy data into a format suitable for security analysis, reducing overall system complexity.
Solution Approach 2:
The system transforms network configuration and policy data into a different parameter representation through the software model. By changing the parameters from raw configuration formats to a standardized software representation format, the system enables precise detection while managing complexity through parameter transformation.
3Ease of manufacture
If traditional activity monitoring and pattern matching are used for threat detection, then the system implementation is simpler and does not require complex software representation generation, but the ability to detect sophisticated attacks and understand network context is limited
Solution Approach 1:
The system performs preliminary action by generating the software representation once from network configuration and policy data before deployment. This upfront effort creates a reusable model that enhances detection capabilities without requiring complex real-time processing during threat detection, maintaining ease of implementation while improving detection power.
Data Source
AI summary
Disclosed is an approach for generating a software representation that embodies network configuration and policy data of a computer network for use in security management. The software representation comprises a state machine where different states can be reached using respective transitions or properties which are possible as determined based on the network configuration and network policy data. The states correspond to respective entities on the network which may comprise resources that are identifiable for protection. The software representation can then be stimulated with various inputs to identify sequences of state-to-state transitions which may in turn be processed to generate corresponding detection signatures for use in monitoring the network.