Malware Detection via Dynamic Behavioral Analysis
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Solution Overview
Problem
Detecting and preventing malware in computer networks is challenging due to constantly evolving software and malware that exploits vulnerabilities, making it difficult for network security analysts to differentiate between secure and compromised nodes, especially when similar or related malware attacks are obfuscated.
Innovation Solution
A system and process for accurately cataloging and classifying malware by generating user interfaces that allow analysts to pivot through data sets, using a database that combines static and dynamic data analysis to identify relationships between malware instances, and providing interactive interfaces for efficient navigation and deployment of security updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If malware analysis relies on traditional signature-based detection, then detection accuracy for known malware is improved, but detection capability against evolving and obfuscated malware deteriorates
Solution Approach 1:
The patent transforms malware analysis from static signature matching to dynamic behavioral parameter monitoring. It captures multiple parameters including API call sequences, registry modifications, file system changes, and network communications during malware execution. By analyzing changes in these parameters over time, the system detects malware based on behavioral patterns rather than fixed signatures, enabling detection of evolved and obfuscated malware while maintaining accuracy for known threats
Solution Approach 2:
The system implements dynamic analysis by executing malware in controlled sandbox environments and monitoring its behavior in real-time. Instead of relying on static characteristics, the system observes dynamic parameters such as process creation, thread execution, memory allocation, and system calls during malware operation. This dynamic approach allows the system to adapt to varying malware implementations and detect polymorphic variants that change their static signatures
2Reliability
If network security monitoring collects comprehensive data from all nodes, then detection coverage is improved, but system complexity and data processing burden increase
Solution Approach 1:
The patent divides the network monitoring system into distributed agents deployed at individual nodes and a centralized analysis server. Each agent independently collects and pre-processes security data locally, segmenting the data collection function across multiple nodes. The centralized server then aggregates these segmented data streams and performs correlated analysis, reducing overall system complexity while maintaining comprehensive detection coverage across the entire network
Solution Approach 2:
The system introduces sandbox environments as intermediary components between malware execution and production networks. These sandboxes act as isolated mediators that capture detailed behavioral data without exposing the main system to malware risks. The intermediary sandboxes simplify the monitoring architecture by providing a controlled buffer zone for data collection and analysis
3Measurement precision
If security analysts manually analyze malware data, then analysis thoroughness is improved, but response time to threats deteriorates
Solution Approach 1:
The system implements automated malware analysis capabilities that perform initial detection, classification, and threat assessment without requiring constant analyst intervention. The automated system services itself by continuously monitoring network traffic, analyzing malware samples, updating detection signatures, and generating alerts. This self-service automation maintains thorough analysis while significantly reducing response time, allowing analysts to focus on complex cases rather than routine detections
Solution Approach 2:
The system establishes feedback loops where analysis results automatically update detection rules and signatures. When malware is detected and analyzed, the system feeds back the learned characteristics into its detection engine, improving future detection accuracy without requiring manual rule updates. This automated feedback mechanism maintains thorough analysis while enabling rapid adaptation to new threats
4Object-affected harmful factors
If malware relationships are obfuscated to prevent detection, then malware evasion capability is improved, but relationship detection by security systems deteriorates
Solution Approach 1:
The patent detects malware relationships by transitioning from analyzing single malware instances to analyzing families and campaigns across multiple dimensions. Instead of examining individual malware characteristics, the system analyzes relationships across temporal dimensions (campaign timelines), spatial dimensions (affected networks and nodes), and behavioral dimensions (attack patterns and techniques). This multi-dimensional approach reveals hidden relationships between obfuscated malware variants by examining their contextual connections rather than direct code similarities
Data Source
AI summary
User interfaces are generated by operations that include receive and store formatted static data and dynamic data. A first query is received, and first response data is selected. A user interface is generated containing the first response data and the user interface is displayed. An indication of user selection is received. A second query is generated and second response data is selected. The user interface is updated to a second user interface, which is displayed.