Device Profile Enrichment Using Inference Rules for Threat Detection
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Solution Overview
Problem
Existing cybersecurity solutions struggle to accurately profile user devices due to the lack of a uniform standard for querying and obtaining device attributes, leading to incomplete data and delayed threat detection, which can result in data breaches and unauthorized access.
Innovation Solution
A method for device profile enrichment using inference rules based on device attribute distributions, which automatically fill in missing or conflicting attributes to create more accurate device profiles, enabling better threat detection and mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If device profiling is performed using existing cybersecurity solutions, then threat detection capability is provided, but profiling accuracy deteriorates due to lack of uniform standards and incomplete device data
Solution Approach 1:
The system performs preliminary actions by proactively inferring missing device attributes before threat detection occurs. Inference rules are applied in advance to populate device profiles with predicted attributes, ensuring complete and accurate profiling data is available when threats need to be detected, thereby resolving the contradiction between having threat detection capability and achieving high profiling accuracy.
Solution Approach 2:
The system introduces an intermediary inference engine that acts as a mediator between incomplete device data and the threat detection system. This intermediary component applies inference rules to bridge gaps in device profiles, transforming partial observations into complete device attribute sets, thereby enabling both reliable threat detection and accurate profiling simultaneously.
2Quantity of substance
If device data is collected from multiple sources, then more device attributes may be obtained, but data completeness worsens due to conflicting and absent attributes from various sources
Solution Approach 1:
The system implements feedback mechanisms where inference rules are continuously applied and refined based on observed device attributes. When attributes are missing or conflicting from multiple sources, the inference engine uses feedback from available data to predict missing values and resolve conflicts, iteratively improving data completeness while maintaining the quantity of attributes collected from diverse sources.
Solution Approach 2:
The system changes parameters by transforming raw device attributes from multiple sources into standardized, inferred attribute values. Inference rules modify and reconcile conflicting parameter values from different sources, converting heterogeneous data into consistent, complete device profiles that maintain attribute quantity while eliminating information loss from conflicts and absences.
3Measurement precision
If inference rules are applied to enrich device profiles, then profiling accuracy is improved, but processing complexity increases
Solution Approach 1:
The system segments the inference process into distinct, modular inference rules that can be independently applied. Each rule handles specific attribute inference tasks, breaking down the complex profile enrichment process into manageable segments. This segmentation maintains high profiling accuracy through comprehensive inference while reducing processing complexity through modular, organized rule application.
4Loss of time
If complete device profiles are obtained, then threat detection timeliness is improved, but data collection difficulty increases due to lack of industry standards
Solution Approach 1:
The inference engine serves as an intermediary that eliminates the need for difficult direct data collection from multiple non-standardized sources. By inferring attributes from available partial data rather than requiring complete direct collection, the system achieves timely threat detection while avoiding the complexities of standardized data collection across diverse device sources.
Data Source
AI summary
Systems and methods for device profile enrichment. A method includes determining a plurality of distributions of device attributes with respect to a plurality of fields of a predefined device profile schema; generating a plurality of inference rules based on the plurality of distributions of device attributes, wherein each inference rule indicates at least one required device attribute and at least one inferred device attribute; creating an ordered set of inference rules including the plurality of inference rules organized with respect to a plurality of scores, each score corresponding to one of the plurality of inference rules, wherein the score for each inference rule is determined based on the at least one required device attribute of the inference rule; and enriching at least one device profile by iterating the ordered set of inference rules, wherein enriching a device profile includes adding at least one device attribute value to the device profile.