Automated Malware Countermeasure Generation via Virtual Machine Event Analysis
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Solution Overview
Problem
Current malware detection techniques are time-consuming and rely on expert intervention, with new malware often evading countermeasures, leading to an endless cycle of creation, detection, and countermeasure development.
Innovation Solution
A method involving a virtual machine with a monitoring subsystem and classifier to generate countermeasures automatically by analyzing event data from malware samples, allowing for quick response to new malware threats without relying on expert updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert analysis is used to develop countermeasures, then detection accuracy is improved, but response time increases
Solution Approach 1:
The system enables self-service by automatically generating countermeasures through machine learning models without requiring expert intervention. The automated countermeasure generation system analyzes malware samples and produces countermeasures autonomously, eliminating the time-consuming manual analysis process while maintaining detection accuracy through algorithmic precision.
Solution Approach 2:
The patent replaces the mechanical system of manual expert analysis with an automated computational system. Machine learning models and algorithms substitute human experts in analyzing malware behavior and generating countermeasures, significantly reducing response time while maintaining or improving detection accuracy through consistent automated evaluation.
2Reliability
If manual countermeasure development is used, then countermeasure quality is improved, but productivity decreases
Solution Approach 1:
The automated system enables continuous countermeasure development by processing malware samples without interruption. Unlike manual processes that stop when experts are unavailable, the automated system continuously analyzes new malware and generates countermeasures in real-time, maintaining both quality through systematic analysis and high productivity through uninterrupted operation.
Solution Approach 2:
The system changes the parameters of countermeasure development by using machine learning models with adjustable parameters to generate countermeasures. This allows the system to optimize countermeasure quality through parameter tuning while simultaneously increasing productivity by eliminating the sequential nature of manual expert review and implementation.
3Loss of time
If automated countermeasure generation is implemented, then response time is reduced, but system complexity increases
Solution Approach 1:
The system reduces complexity through segmentation by dividing the countermeasure generation process into distinct modular components: malware analysis module, machine learning model module, countermeasure generation module, and deployment module. Each segment handles a specific task independently, making the overall complex system manageable and maintainable while enabling rapid automated response.
Solution Approach 2:
The patent uses intermediary elements such as standardized data formats, API interfaces, and pre-trained machine learning models to bridge different system components. These intermediaries simplify interactions between modules, reducing the perceived complexity while enabling fast automated countermeasure generation and deployment.
Data Source
AI summary
Malware can be automatically detected and countermeasures automatically generated. A virtual machine (VM) is run with an operating system configured with a monitoring subsystem. The monitoring subsystem is configured to generate event data based on events occurring on the virtual machine. The monitoring subsystem can run within the operating system kernel. Kernel drivers can register to receive specific events. The events are therefore sent to the drivers, which can send them to a classifier. The classifier can detect malware based on the events. When a sample is run on the VM, the classifier can detect malware in the sample. While running the sample, event data is collected. A countermeasure compiler can generate a countermeasure to the malware, the countermeasure based on the event data.