Malware Detection via Dynamic Behavioral Analysis
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Solution Overview
Problem
Sophisticated malware evades detection by using encryption, packing, and evasive techniques, leading to failures in current security systems, resulting in data loss, downtime, and high recovery costs.
Innovation Solution
A security server analyzes the execution of suspicious data to detect triggers indicating unpacked malware, extracts portions for further analysis, and classifies them using machine-learning based models, eliminating the need to fully unpack malware and reducing reliance on knowledge of packing or encryption techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If malware is encrypted or packed to avoid detection, then the malware's ability to evade detection improves, but the difficulty of analyzing and classifying the malware increases
Solution Approach 1:
The patent introduces a sandbox environment as an intermediary between the malware and the analysis system. The sandbox isolates the malware execution, allowing safe observation of behavioral patterns without direct interaction with the host system. This mediator enables the analysis of encrypted/packed malware through its runtime behavior rather than requiring static decryption or unpacking, thus resolving the contradiction between detection reliability and analysis complexity
Solution Approach 2:
The patent replaces traditional mechanical/static analysis methods (manual code review, static signature matching) with dynamic behavioral analysis through machine learning models. Instead of physically examining and decrypting malware code, the system observes execution patterns, system calls, and behavioral characteristics in a sandboxed environment, substituting complex mechanical analysis with automated behavioral pattern recognition
2Reliability
If traditional antivirus products are used to detect malware, then detection capability is maintained, but sophisticated malware can evade detection through evasive techniques
Solution Approach 1:
The patent transitions from static signature-based detection to dynamic behavioral analysis. Instead of relying on fixed malware signatures that sophisticated malware can easily evade, the system continuously monitors runtime behavior, system calls, and execution patterns. This dynamic approach adapts to different malware variants by learning from observed behaviors, making it difficult for malware to evade detection through static evasive techniques
Solution Approach 2:
The patent changes the fundamental parameters of malware analysis from static code properties (signatures, hashes) to dynamic behavioral parameters (execution flow, system calls, memory access patterns). By analyzing behavioral parameters rather than code structure, the system can detect malware regardless of encryption or packing techniques, as these transformations do not fundamentally alter the malware's behavioral characteristics during execution
3Loss of information
If full unpacking of malware is performed for analysis, then complete malware analysis is achieved, but analysis time and computational resources increase significantly
Solution Approach 1:
The patent extracts only the essential behavioral information needed for classification from the malware execution process. Instead of fully unpacking and analyzing the entire malware payload, the system extracts key behavioral parameters, system call sequences, and execution patterns that are sufficient for accurate classification. This selective extraction of critical information maintains analysis effectiveness while dramatically reducing the time and resources required compared to complete unpacking and static analysis
Data Source
AI summary
A network device may include a memory and one or more processors configured to analyze execution of suspicious data; detect one or more states of execution of the suspicious data; determine that the one or more states of execution are to be assigned a priority level; and extract at least a portion of the suspicious data from one or more locations based on determining that the one or more states of execution are to be assigned a priority level.