Real-Time Malware Detection Through Stateful Behavior Models
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Solution Overview
Problem
Current malware detection technologies, including signature-based and behavior-based analysis, are ineffective against modified malware variants and fail to detect malicious behaviors in real-time due to resource-intensive emulation and limited observation of execution in live environments.
Innovation Solution
A real-time malware detection system that monitors operations in a live environment, builds stateful models from these operations, and analyzes behaviors using predefined logics to identify malware presence, employing in-process and kernel-level monitoring with stateful models to detect malicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If behavior-based analysis is used to detect malware variants with similar effects, then detection capability for malware variants is improved, but system complexity and resource consumption increase due to comprehensive behavior monitoring
Solution Approach 1:
The patent segments behavior monitoring into distinct categories (file system operations, registry operations, network operations, process operations) and uses separate monitoring mechanisms for each category. This allows the system to detect malware behaviors across multiple dimensions while managing complexity through organized, modular monitoring structures.
Solution Approach 2:
The patent monitors only specific behaviors and operations that are most indicative of malware activity, rather than all possible system operations. By focusing on partial monitoring of critical behaviors (file creation, registry changes, network connections, process spawning), the system achieves effective detection without the resource burden of comprehensive monitoring.
2Measurement precision
If emulation is used to monitor malware execution, then behavior detection accuracy is improved, but resource consumption increases and real-time detection capability deteriorates
Solution Approach 1:
The patent introduces an intermediary monitoring layer that observes malware behaviors indirectly through system calls and operations rather than directly executing and analyzing the malware in an emulator. This intermediary approach captures behavioral data (file operations, registry changes, network activity) without the resource-intensive process of full emulation, enabling real-time detection.
3Productivity
If signature-based analysis is used to identify malware, then detection speed is improved, but effectiveness against modified malware variants deteriorates
Solution Approach 1:
The patent transitions from static signature matching to dynamic behavior monitoring that adapts to malware variations. By observing the operational behaviors of malware (file creation patterns, registry modifications, network connections, process spawning), the system can identify malicious intent regardless of signature changes, obfuscation, or randomization techniques used by malware variants.
Data Source
AI summary
There is provided a system and a computer-implemented method of detecting malware in real time in a live environment. The method comprises: monitoring one or more operations of at least one program concurrently running in the live environment, building at least one stateful model in accordance with the one or more operations, analyzing the at least one stateful model to identify one or more behaviors, and determining the presence of malware based on the identified one or more behaviors.