Isolated Virtual Environment for Pre-Execution Malware Detection
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Solution Overview
Problem
Current defense mechanisms against malware, particularly ransomware, are reactive and often fail to detect the onset of malicious activity until it is too late, allowing malware to execute and compromise systems.
Innovation Solution
A system that continuously monitors incoming messages for assessment vectors, redirects them to an isolated virtual environment for emulation, and uses machine learning to determine malware presence, enabling immediate detection and mitigation before reaching the intended recipient.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current reactive defense mechanisms are used to detect malware, then detection can occur after malware execution, but system compromise has already happened
Solution Approach 1:
The system performs preliminary actions by continuously monitoring incoming messages and detecting assessment vectors before malware execution can occur. The isolated virtual environment is proactively initiated to contain potential threats, and machine learning models pre-analyze message characteristics to identify malicious content ahead of time, preventing system compromise rather than detecting it after the fact.
Solution Approach 2:
An isolated virtual environment acts as an intermediary between incoming messages and the host system. This sandboxed environment receives and analyzes potentially malicious messages, allowing the system to detect and mitigate threats without exposing the main system to risk. The intermediary enables safe examination of assessment vectors and malware behavior.
2Measurement precision
If continuous monitoring of all incoming messages is implemented, then malware detection capability improves, but system resource consumption increases
Solution Approach 1:
The system applies partial action by focusing monitoring efforts on specific assessment vectors within incoming messages rather than analyzing every single message in full detail. The machine learning models are trained to detect specific patterns and characteristics associated with malware, enabling precise detection without the need to exhaustively analyze every message, thus reducing computational overhead while maintaining high detection precision.
Solution Approach 2:
The machine learning models perform self-service by automatically analyzing assessment vectors and making determinations about malware presence without requiring extensive manual intervention or complex processing. The models have been pre-trained to independently evaluate message characteristics and classify threats, reducing the need for resource-intensive real-time analysis of every message component.
3Object-affected harmful factors
If an isolated virtual environment is initiated for each incoming message with assessment vectors, then malware containment effectiveness improves, but device complexity increases
Solution Approach 1:
The system extracts potentially malicious assessment vectors from incoming messages and isolates them in a separate virtual environment. By taking out the harmful components and containing them in a sandboxed space, the system prevents malware from affecting the host system while maintaining manageable complexity through focused isolation rather than comprehensive system-wide virtualization.
Solution Approach 2:
The isolated virtual environment provides local quality by creating a specialized containment space with specific properties tailored for malware analysis. This localized environment has the necessary resources and configurations to safely execute and analyze malicious content, while the rest of the system maintains its normal operational characteristics, thus managing complexity through targeted rather than universal virtualization.
Data Source
AI summary
Systems, computer program products, and methods are described herein for active detection and mitigation of unauthorized activity within a technology infrastructure. The present invention is configured to continuously monitor one or more incoming messages in one or more computing devices; detect one or more assessment vectors embedded in the one or more incoming messages; initiate an isolated virtual environment; redirect the one or more incoming messages associated with the one or more assessment vectors from the one or more computing devices to the isolated virtual environment; trigger an access routine to emulate, within the isolated virtual environment, an action of accessing the one or more incoming messages; determine, based on at least the access routine, whether the one or more incoming messages is associated with malware; and display a notification to the user indicating whether the one or more incoming messages is associated with malware.