ML Exploit Prevention Settings for Process Stability
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Solution Overview
Problem
Conventional exploit prevention software often causes processes to crash or become unstable due to inadequate configuration settings, leading to inefficiencies and human error in manual adjustments.
Innovation Solution
Implementing machine learning systems to determine and dynamically adjust exploit prevention software settings based on process metadata, using models to generate configuration settings and monitor stability, creating a feedback loop for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exploit prevention software is implemented with manual configuration settings, then exploit protection capability is improved, but system stability deteriorates due to process crashes and instability
Solution Approach 1:
The system implements a feedback loop where stability data from monitored processes is transmitted to the machine learning system, which then modifies the exploit prevention model based on this feedback. This allows the system to learn from actual process behavior and adjust configuration settings to maintain both protection capability and process stability.
Solution Approach 2:
The machine learning system automatically generates and adjusts exploit prevention configuration settings without requiring manual intervention. The system serves itself by using the exploit prevention model to determine optimal settings based on process metadata and stability data, eliminating the need for manual configuration while maintaining both protection and stability.
2Reliability
If manual configuration settings are applied to each affected process, then exploit protection is improved, but time consumption increases significantly
Solution Approach 1:
The machine learning system automatically generates exploit prevention configuration settings for multiple processes simultaneously without requiring manual intervention for each process. This self-service approach dramatically reduces the time required to configure exploit prevention while maintaining comprehensive protection capability across all affected processes.
Solution Approach 2:
The exploit prevention model serves multiple functions by automatically generating configuration settings for different processes based on their metadata. This universal system can handle various process types and scenarios without requiring separate manual configuration efforts, significantly improving efficiency.
3Stability of the object's composition
If manual configuration adjustments are made to prevent process crashes, then process stability is improved, but human error increases the likelihood of incorrect settings
Solution Approach 1:
The machine learning system eliminates human error by automatically generating configuration settings based on process metadata and stability data. The system uses trained models to make precise determinations about optimal settings, replacing manual human judgment with algorithmic precision that consistently produces accurate configurations.
Solution Approach 2:
The system replaces manual human configuration operations with an automated machine learning-based system. This substitution transforms the mechanical process of manual setting adjustment into an automated computational process that uses algorithms to determine optimal configurations, thereby eliminating human error while improving configuration accuracy.
Data Source
AI summary
Examples of the present disclosure describe systems and methods for determining exploit prevention software settings using machine learning. In aspects, exploit prevention software may be used to identify processes executing on a computing device. Metadata for the identified processes may be determined and transmitted to a machine learning system. The machine learning system may use an exploit prevention model to determine exploit prevention configuration settings for each of the processes, and may transmit the configuration setting to the computing device. The computing device may implement the configuration settings to protect the processes and monitor the stability of the protected processes as they execute. The computing device may transmit the stability data to the machine-learning system. The machine-learning system may then modify the exploit prevention model based on the stability data.