Behavioral Threat Detection Virtual Machine for Dynamic Malware Analysis
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Solution Overview
Problem
Traditional malware detection techniques rely on signature-based analyses, which are slow to adapt to new threats and ineffective against obfuscated files, and fail to detect threats that do not originate from locally stored files, leading to inadequate security.
Innovation Solution
A behavioral threat detection virtual machine that executes rules comprising instructions to evaluate events and maintain a state, allowing for dynamic identification of malicious behaviors without relying on rigid file-based approaches, using a rule virtual machine to pause and resume execution based on event matches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based analysis is used for malware detection, then detection accuracy for known threats is improved, but adaptability to new threats deteriorates
Solution Approach 1:
The patent transitions from static signature-based detection to dynamic behavioral analysis. The system monitors runtime behavior of programs, allowing it to adapt to new threats by observing actual execution patterns rather than relying on pre-defined signatures. This enables the system to detect previously unknown malware by comparing its behavior against established safe behavior profiles.
Solution Approach 2:
The patent changes the detection parameter from file-based signatures to behavior-based metrics. Instead of analyzing static file properties, the system monitors dynamic parameters such as system calls, registry modifications, and process interactions during execution, fundamentally shifting the detection approach to one that adapts to new threat patterns.
2Reliability
If signature-based malware detection is implemented, then detection of known threats is improved, but response time to new threats deteriorates
Solution Approach 1:
The patent performs preliminary behavioral analysis by establishing safe behavior profiles before threats occur. The system proactively monitors and records normal program behavior patterns, creating a baseline that enables immediate detection of deviations. This preliminary action eliminates the time lag associated with signature updates, as the system is already equipped with behavioral knowledge to detect new threats upon their first appearance.
3Difficulty of detecting and measuring
If traditional file-based detection methods are used, then detection capability for local threats is improved, but detection of remote threats deteriorates
Solution Approach 1:
The patent creates a universal detection system that functions across multiple threat vectors. By monitoring behavioral patterns rather than file locations, the system achieves multi-functionality, detecting threats whether they originate from local files, remote downloads, or memory-based attacks. The behavioral analysis approach is universally applicable to any executable code regardless of its source or storage location.
4Adaptability or versatility
If behavioral threat detection virtual machine is implemented, then adaptability to new threats is improved, but device complexity increases
Solution Approach 1:
The patent introduces a virtual machine as an intermediary layer between the host system and the analysis engine. This virtualized environment isolates behavioral analysis activities, providing a controlled sandbox for monitoring program execution without affecting the host system. The intermediary VM simplifies complexity management by containing the sophisticated behavioral analysis logic in a separate, manageable layer that can be independently configured and maintained.
Data Source
AI summary
Examples of the present disclosure describe systems and methods for a behavioral threat detection virtual machine. In examples, the virtual machine executes a rule comprising rule instructions. A rule may comprise one or more wait rule instructions that causes the virtual machine to pause execution. As events are added to an event queue for the rule virtual machine, the behavioral threat detection virtual machine evaluates such events in order to identify a positive or, in some instances, a negative match. When a matching event is identified, rule execution resumes. Eventually, a determination is made as a result of processing events and wait packets, thereby indicating the presence or absence of a malicious or potentially malicious behavior, among other examples. Thus, among other things, the behavioral threat detection virtual machine maintains a state associated with rule execution and processes events to identify behaviors accordingly.