DHCP Option Sequence Profiling for Accurate Device Attribute Identification
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Solution Overview
Problem
Existing cybersecurity solutions struggle to accurately profile device attributes due to the lack of a uniform standard for querying device information, leading to incomplete and conflicting data, especially with the rise of new devices and varying data sources, which complicates identifying operating systems and detecting potential threats.
Innovation Solution
Utilizing machine learning models trained on host configuration protocol data, particularly DHCP option 55 packets, to normalize, filter, and group data for improved device attribute identification, enhancing accuracy and scalability in determining device attributes like operating systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional device profiling methods are used to identify device attributes, then device information can be obtained from available sources, but the data is incomplete and conflicting due to lack of uniform standards
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between diverse device data sources and the profiling process. This intermediary standardizes and normalizes device attribute data from multiple sources, resolving conflicts and filling gaps through a unified data processing layer that applies consistent interpretation rules across different device types and data formats
Solution Approach 2:
The patent transforms device identification from relying on raw, heterogeneous device attributes to using standardized, normalized parameters. By changing the parameter representation and applying consistent transformation rules, the system converts incomplete and conflicting raw data into reliable, standardized device profiles that can be accurately interpreted
2Quantity of substance
If multiple data sources are used to collect device information, then more device attributes can be gathered, but data conflicts and inconsistencies increase
Solution Approach 1:
The patent merges data from multiple sources by applying a unified normalization framework that combines device attributes into a single standardized representation. This merging process resolves conflicts through consistent interpretation rules and produces a unified device profile that maintains reliability while incorporating information from multiple data sources
Solution Approach 2:
The patent creates a universal data processing framework that handles multiple device types and data sources through a single standardized interface. This multi-functional system applies consistent normalization rules across diverse inputs, ensuring reliability and consistency regardless of the number or type of data sources used
3Adaptability or versatility
If new types of devices are supported, then device coverage increases, but identification accuracy decreases due to lack of standards
Solution Approach 1:
The patent implements a universal profiling framework that can handle diverse device types through standardized attribute normalization. This multi-functional system applies consistent interpretation rules across different device categories, maintaining identification accuracy while expanding coverage to include new and varied device types without requiring device-specific customization
Solution Approach 2:
The patent employs dynamic normalization rules that adapt to different device types while maintaining consistent output standards. The system dynamically adjusts its processing based on device characteristics but applies unified interpretation principles, allowing versatile device coverage while preserving identification accuracy through flexible yet consistent data transformation
Data Source
AI summary
A system and method for determining device attributes based on host configuration protocols. A method includes applying at least one machine learning model to a test data set extracted from host configuration protocol data including at least one test options sequence, wherein each test options sequence is an ordered series of options requested by a first device, wherein each of the at least one machine learning model is trained based on a train data set including a plurality of training options sequences and a plurality of device attributes, wherein each training options sequence and each device attribute of the train data set corresponds to a respective second device; and determining, based on the output of the at least one machine learning model, at least one device attribute for the first device.