Malicious Content Detection via Virtual Machine Memory Dump Analysis
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Solution Overview
Problem
Current malware detection methods are ineffective against polymorphic malware and encrypted malicious content, as they often require manual signature updates and may not detect threats in a timely manner, leading to potential damage and propagation of malware.
Innovation Solution
A malicious content detection system that executes suspicious content in a virtual machine, captures a memory dump during execution, and analyzes it to identify malicious activities, allowing for the extraction and analysis of embedded or encrypted content, even after decryption, using a file extraction mechanism to access and analyze files stored during execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based scanning is used to detect malware, then known malware can be identified, but polymorphic malware and encrypted threats cannot be detected
Solution Approach 1:
The system performs preliminary actions by executing the suspicious file in a virtual machine environment before actual analysis. This allows the malware to decrypt and decompress its content in advance, capturing the memory dump at a state where encrypted content has been decrypted but before the system is permanently compromised. This preliminary execution resolves the contradiction by making hidden malicious content visible for detection.
Solution Approach 2:
The patent introduces a virtual machine as an intermediary environment between the analysis system and the malware. The VM acts as a safe mediator that allows malicious content to execute and reveal its true nature without directly affecting the host system. This intermediary enables detection of polymorphic and encrypted malware by providing a controlled space where their transformations can be observed and captured.
2Reliability
If manual signature updates are performed, then detection accuracy for known malware is maintained, but detection is delayed by days or weeks
Solution Approach 1:
The system performs self-service by automatically analyzing suspicious files through virtual machine execution without requiring manual signature updates. The automated analysis process independently determines whether content is malicious by observing behavior and extracting content from memory dumps, eliminating the time delay associated with manual signature creation, library updates, and distribution to scanning software.
Solution Approach 2:
The system performs preliminary analysis actions immediately when suspicious content is detected, rather than waiting for signature updates. By executing the file in a VM and analyzing its behavior in real-time, the system achieves timely detection without the days or weeks delay inherent in manual signature update processes.
3Adaptability or versatility
If virtual machine execution is used to analyze malware, then encrypted and polymorphic content can be detected, but system complexity increases
Solution Approach 1:
The system applies segmentation by separating the analysis environment from the host system through virtual machine technology. The VM creates an isolated segment where malicious content can execute safely, while the host system remains protected. This segmentation enables comprehensive detection of all malware types while managing complexity by containing potential harm within a discrete virtual environment that can be easily created, managed, and destroyed.
4Measurement precision
If memory dumps are captured and analyzed, then hidden malicious content can be extracted, but analysis time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by capturing the memory dump at an optimal moment during VM execution - after the malware has had chance to decrypt and decompress its content but before it can cause significant harm or propagate. This timing strategy ensures that hidden malicious content is already revealed in the memory dump, reducing the need for extensive additional analysis and minimizing overall analysis time while maintaining high detection precision.
Data Source
AI summary
A network device for detecting malware is described. The network device features a memory storage device and a controller. The controller operating in cooperation with one or more virtual machines that are based on software modules stored within the memory storage device. The controller is configured to (i) monitor behaviors of at least a first virtual machine of the one or more virtual machines processing data received over a network, (ii) identify at least one anomalous behavior that includes either a communication anomaly or an execution anomaly, and (iii) detect, based on the identified at least one anomalous behavior, a presence of malware in the first virtual machine in response to identifying the at least one anomalous behavior.