Endpoint Security for Malicious Configuration Profiles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting malicious distributed configuration profiles in computers often result in high false positive rates, as they are not specifically designed to handle configuration profiles, which are inherently normal data, leading to incorrect detection of safe data as malicious.
Innovation Solution
A supervised decision tree is traversed using XML keys and values of distributed configuration profiles to generate a rating score, with weights assigned to nodes, and cybersecurity intelligence is used to adjust the score, minimizing false positives and effectively detecting malicious profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing detection methods are used to identify malicious configuration profiles, then security detection capability is improved, but false positive rate increases
Solution Approach 1:
The patent transforms the configuration profile data into numerical features by extracting XML elements and attributes, then applies machine learning models that operate on numerical parameters rather than raw text data. This parameter transformation enables precise detection while reducing false positives by converting qualitative security assessment into quantitative measurement.
Solution Approach 2:
The patent replaces traditional rule-based detection mechanisms with machine learning models (random forest, support vector machines, neural networks). This substitution allows the system to learn complex patterns in configuration profiles that rule-based systems miss, improving detection accuracy while reducing false positives through adaptive pattern recognition.
2Ease of operation
If configuration profiles are distributed and automatically installed to simplify device management, then ease of operation is improved, but security risk increases
Solution Approach 1:
The patent implements pre-installation security assessment by analyzing configuration profiles before they are automatically installed. The machine learning model evaluates profiles in advance, generating security scores that determine whether installation should proceed. This preliminary action maintains automation benefits while preventing malicious profiles from being installed.
Solution Approach 2:
The patent introduces an intermediary security assessment layer between profile distribution and installation. The machine learning-based evaluation system acts as a mediator that automatically screens profiles, allowing safe profiles to pass through unchanged while blocking malicious ones. This intermediary preserves the automated distribution workflow while adding security validation.
Data Source
AI summary
An endpoint computer is protected from malicious distributed configuration profiles. The endpoint computer receives a distributed configuration profile over a computer network. Before installation of the distributed configuration profile in the endpoint computer, features of the distributed configuration profile are used to traverse a supervised decision tree. A rating score is generated based on weights of nodes of the supervised decision tree that are traversed using the features of the distributed configuration profile. The distributed configuration profile is detected to be malicious based at least on the rating score.