Malware Detection via Memory Dump Semantic Analysis
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Solution Overview
Problem
Existing malware detection systems rely on signature-based methods, which are ineffective against packed malware and those that detect sandbox environments, leading to a need for improved detection and classification methods.
Innovation Solution
The system performs semantic analysis of memory dumps to identify malware by executing a sample file in a monitored process, capturing memory dumps, and analyzing their characteristics against known malware samples, enabling detection of packed malware and behavior-based evasion techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based detection methods are used, then known malware can be detected, but packed malware and malware evading sandbox environments cannot be detected
Solution Approach 1:
The patent creates a virtual copy of the malware sample by executing it in a controlled sandbox environment and capturing memory dumps. This copy allows analysis of the malware's actual behavior and memory characteristics without exposing the real system to threats, enabling detection of packed and evading malware through semantic analysis of the captured memory state.
Solution Approach 2:
The patent replaces traditional signature-based mechanical detection with semantic analysis of memory dumps. Instead of relying on static signature matching, the system analyzes the semantic meaning and characteristics of code executed in memory, enabling detection of obfuscated and packed malware that bypasses traditional signature-based systems.
2Reliability
If behavior-based detection systems are used, then malware behavior can be monitored, but malware that detects sandbox environments and refuses to run cannot be detected
Solution Approach 1:
The patent performs preliminary actions by capturing memory dumps at specific points during malware execution before the malware can detect and evade the sandbox environment. By analyzing the memory state at these critical moments, the system can identify malware behavior patterns even when the malware attempts to refuse execution or evade detection.
Solution Approach 2:
The patent introduces memory dumps as an intermediary between the sandbox environment and the detection system. These memory dumps serve as a mediator that captures the malware's state and behavior without requiring direct observation of the malware's execution flow, allowing detection even when the malware attempts to evade sandbox environments.
3Reliability
If memory dump semantic analysis is performed, then packed and evading malware can be detected, but system complexity increases
Solution Approach 1:
The patent segments the complex analysis process into distinct components: sandbox environment for execution, process monitor for capturing memory dumps, and semantic analysis engine for interpreting the dumps. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by dividing the complex detection task into manageable modules.
Solution Approach 2:
The patent creates a universal detection system that can handle multiple malware types (known, packed, evading) through a single unified approach of memory dump semantic analysis. This multi-functional system replaces multiple specialized detection mechanisms with one versatile framework that adapts to different malware behaviors through consistent semantic analysis of memory state.
Data Source
AI summary
Systems and methods for malware detection and classification based on semantic analysis of memory dumps of malware are provided. According to one embodiment, a malware detector running within a computer system causes a sample file to be executed within a target process that is monitored by a process monitor of the malware detector. One or more memory dumps associated with the sample file are captured by the process monitor. A determination regarding whether the sample file represents malware is made by the malware detector by analyzing characteristics of at least one memory dump of the one or more memory dumps with reference to characteristics of memory dumps of a plurality of known malware samples.