In-Memory Malware Unpacking for Obfuscated Threat Detection
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Solution Overview
Problem
Conventional sandbox techniques struggle to detect malware that employs obfuscation and encryption, as threat actors evade detection by hiding malicious intent until activation.
Innovation Solution
An in-memory malware unpacking and deobfuscation method that scans, unpacks, and deobfuscates unknown content in a cloud-based sandbox, monitoring execution events and adjusting scores based on behavioral analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sandbox techniques monitor generic characteristics of malware, then the detection system remains simple and fast, but it fails to detect obfuscated and encrypted malware that hides malicious intent
Solution Approach 1:
The system performs preliminary unpacking and deobfuscation of malware samples before analysis. By extracting and decoding packed/encrypted code in advance, the system reveals hidden malicious intent that would otherwise remain concealed during standard sandbox monitoring, thereby improving detection accuracy without requiring fundamental changes to the sandbox architecture
Solution Approach 2:
The patent introduces intermediary processing components that act as mediators between the sandbox environment and the analysis system. These intermediaries handle the complex tasks of unpacking, deobfuscation, and decoding, allowing the core sandbox to remain relatively simple while still achieving high detection accuracy through the added preprocessing layer
2Measurement precision
If the system unpacks and deobfuscates code in real-time, then detection capability improves, but processing time and computational resources increase
Solution Approach 1:
The system performs unpacking and deobfuscation operations before the malware executes in the sandbox environment. By preparing the code in advance and extracting its true functionality prior to behavioral analysis, the system avoids time-consuming operations during critical detection phases, thereby reducing overall processing time while maintaining enhanced detection capability
Solution Approach 2:
The analysis process is divided into distinct segments: initial scanning for packed/obfuscated code, unpacking/deobfuscation phase, and sandbox execution phase. This segmentation allows the system to process different aspects of malware analysis in parallel or sequential batches, optimizing resource utilization and reducing total processing time while maintaining comprehensive detection
3Measurement precision
If the system monitors execution events and performs behavioral analysis, then detection accuracy improves, but the complexity of analysis and scoring increases
Solution Approach 1:
The system implements feedback mechanisms where execution events from sandbox monitoring are continuously analyzed and fed back into the detection system. Behavioral patterns, system calls, and execution flows are tracked and used to dynamically adjust detection scores. This feedback loop enables accurate detection of obfuscated malware by comparing actual behavior against expected benign patterns, improving detection accuracy while managing analysis complexity through iterative refinement
Data Source
AI summary
Systems and methods for in-memory malware unpacking and deobfuscation in a sandbox include, responsive to receiving unknown content, scanning an image of the unknown content for packed, obfuscated, or encrypted code; responsive to detecting the packed, obfuscated, or encrypted code performing steps of unpacking, deobfuscating, or decrypting the packed, obfuscated, or encrypted code; executing the unpacked, deobfuscated, or decrypted code; monitoring execution of the unpacked, deobfuscated, or decrypted code; obtaining events during the scanning and the execution; and providing the obtained events to the sandbox for use in a sandbox analysis for classifying the content as one of malware and clean.