Endpoint Security Verification Using ML-Based Test Selection
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Solution Overview
Problem
Existing security testing approaches for network devices are inefficient and ineffective due to the need for customized tests based on varying device configurations, leading to potential security vulnerabilities and performance issues.
Innovation Solution
A system utilizing machine learning to determine customized security tests based on individual device configurations, with automated execution and validation, and the option to update devices based on validation ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a one-size-fits-all security testing approach is used across all network devices, then the testing process is simplified and can be applied uniformly, but it fails to address device-specific configurations and applications, leading to ineffective security validation
Solution Approach 1:
The system implements local quality by analyzing individual device configurations (operating system, applications, hardware) and generating customized security test suites tailored to each device's specific characteristics. This ensures that each device receives security testing relevant to its unique configuration rather than a generic approach.
Solution Approach 2:
The system employs dynamic test suite generation that adapts to each device's configuration. The machine learning model dynamically selects and prioritizes tests based on real-time analysis of device attributes, making the testing process flexible and responsive to individual device needs rather than static and uniform.
2Reliability
If customized security tests are developed for each device configuration, then security validation effectiveness is improved, but the complexity and time required for test development and execution increases significantly
Solution Approach 1:
The system implements self-service by automatically analyzing device configurations and generating appropriate security test suites without requiring manual intervention. The machine learning model autonomously processes device data, selects relevant tests, and prioritizes them based on security risk, eliminating the need for complex manual test development for each device.
Solution Approach 2:
The system changes parameters by using machine learning to dynamically adjust test selection and prioritization based on device configuration parameters. The model processes multiple device attributes (OS type, applications installed, hardware specs) and transforms these into optimized test suite parameters, managing complexity through intelligent parameter transformation rather than manual configuration.
3Reliability
If comprehensive security tests are executed on all devices, then security coverage is maximized, but the time and computational resources required increase substantially
Solution Approach 1:
The system applies partial action by executing only the most relevant security tests for each device based on its configuration. The machine learning model identifies and prioritizes critical tests that provide the highest security value for specific device types, avoiding unnecessary tests that would consume time and resources without adding proportional security value.
Solution Approach 2:
The system segments the security testing process into device-specific test suites. Rather than executing a monolithic comprehensive test suite on all devices, the machine learning model divides tests into targeted groups based on device configuration, allowing parallel execution of specialized test sets that collectively provide comprehensive coverage across the device fleet while reducing individual device test time.
4Adaptability or versatility
If manual security testing approaches are used, then flexibility in handling different device configurations is maintained, but productivity and automation level remain low
Solution Approach 1:
The system implements universality through a unified machine learning-based platform that handles diverse device configurations through a single automated process. The system universally processes different device types (mobile, desktop, IoT) using the same core architecture, automatically adapting test selection to each device's specific characteristics while maintaining consistent security standards across all platforms.
Solution Approach 2:
The system replaces manual mechanical testing processes with automated machine learning-based test generation and execution. The machine learning model automates the analysis of device configurations and selection of appropriate security tests, substituting human analysts with an automated intelligent system that processes configurations and executes tests at scale, dramatically increasing productivity while maintaining adaptability.
Data Source
AI summary
Systems, computer program products, and methods are described herein for automated security verification across network devices. The method includes determining a first end-point device configuration of a first end-point device associated with a network. The method also includes determining, via a machine learning model, one or more first end-point device tests based on the first end-point device configuration. The one or more first end-point device tests is based on at least one of the one or more first end-point device programs installed on the first end-point device. The method further includes causing an execution of at least one of the one or more first end-point device tests on the first end-point device. The method still further includes determining a first end-point device validation rating based on the execution of the at least one of the one or more first end-point device tests on the first end-point device.