Malware Detection Framework Using Dynamic Behavior Analysis
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Solution Overview
Problem
Traditional malware detection techniques often fail to detect new variants of malware families, as they rely on static signatures and do not effectively identify behavior patterns common among variants.
Innovation Solution
A method involving a secure environment to execute data samples associated with malware families, identifying attributes indicative of the malware, and classifying them as strong, moderate, or weak indicators to enhance detection capabilities within a framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional static signature-based detection is used, then detection speed is fast, but detection accuracy for new malware variants deteriorates
Solution Approach 1:
The patent transitions from static signature-based detection to dynamic behavior-based detection. The system executes malware samples in a sandbox environment and monitors their runtime behavior (file operations, registry changes, network connections) to identify malware family characteristics, enabling detection of new variants without relying on pre-existing static signatures.
Solution Approach 2:
The patent replaces the mechanical approach of pattern matching static signatures with a computational approach of executing code in a controlled environment and observing behavior. This substitution allows the system to detect malware based on functional characteristics rather than fixed patterns, improving accuracy for unknown variants.
2Measurement precision
If behavior-based detection is implemented, then detection accuracy for malware variants improves, but processing time increases
Solution Approach 1:
The patent segments the malware detection process into distinct phases: (1) executing the malware sample in a sandbox environment, (2) monitoring and collecting behavior attributes (file operations, registry modifications, network connections), (3) comparing collected attributes against known malware family characteristics, and (4) generating detection results. This segmentation enables efficient processing by focusing on specific behavior categories rather than analyzing all possible system states.
Solution Approach 2:
The patent applies partial action by monitoring only the most relevant behavior attributes associated with malware families (such as file creation, registry keys, network sockets) rather than comprehensively tracking all possible system behaviors. This selective monitoring reduces processing overhead while maintaining detection accuracy for the target malware family.
3Reliability
If comprehensive attribute monitoring is performed, then detection completeness improves, but system complexity increases
Solution Approach 1:
The patent implements a universal monitoring framework that can detect multiple malware families by observing common behavior patterns. The same sandbox environment and attribute collection mechanism work across different malware types, eliminating the need for separate specialized monitoring systems for each malware family while maintaining comprehensive detection capability.
Solution Approach 2:
The patent changes the detection parameters from static binary signatures to dynamic behavioral attributes. By monitoring attributes such as file operation types, registry key modifications, network connection patterns, and process creation sequences, the system can reliably identify malware families based on their functional characteristics rather than fixed identifiers, improving reliability without requiring family-specific monitoring configurations.
Data Source
AI summary
Embodiments disclosed herein are directed to intelligent malware detection. A scanner server is used to scan an endpoint device for malware. Various attributes and behaviors of the endpoint device are identified in retrieved scan data. Identified attributes and behaviors are then evaluated according to a malware detection framework, which is used to determine whether (as well as to what extent) the identified attributes and behaviors are indicative of malware. In this manner, potential security risks associated with the malware may be identified. The framework is constructed through a machine learning process that aggregates attributes and behaviors common amongst members of malware families. Advantageously, the framework enables the scanner server to detect unknown variants of known malware families.