Malware Memory Image Analysis for Capability Forecasting
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Solution Overview
Problem
Current malware analysis techniques are inefficient and cognitively burdensome, requiring repeated context switching between tools, and struggle to predict post-detection malware capabilities due to lack of access to the execution context, especially for fileless malware.
Innovation Solution
A method that captures a malware memory image, reconstructs the prior execution context, and uses symbolic analysis to model and predict possible execution paths, assigning probabilities to each path and matching APIs to capability analysis plugins to forecast staged malware capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual malware analysis is performed using multiple tools (static disassembler, memory forensics), then malware capabilities can be identified, but cognitive load increases and investigation speed decreases
Solution Approach 1:
The patent combines static analysis (disassembly) and dynamic analysis (memory forensics) into a unified automated framework. The system integrates multiple analysis tools and techniques, merging their outputs to comprehensively identify malware capabilities without requiring manual context switching between tools.
Solution Approach 2:
The system performs self-service by automatically executing the complete malware analysis workflow without human intervention. It autonomously disassembles malware, extracts memory artifacts, reconstructs execution context, and identifies capabilities, thereby eliminating the need for analyst involvement in routine analysis tasks.
2Adaptability or versatility
If symbolic execution is used for malware code exploration, then code paths can be analyzed, but prior attack execution state is lost and inaccessible capabilities cannot be predicted
Solution Approach 1:
The system performs preliminary action by capturing memory artifacts and reconstruction of execution context before symbolic execution begins. This preserves the prior attack execution state (concrete inputs from C&C activity, environment-specific conditions) that would otherwise be lost, enabling more accurate prediction of staged capabilities.
Solution Approach 2:
The patent introduces memory artifact analysis as an intermediary between dynamic execution and symbolic analysis. The captured memory images serve as a bridge, preserving concrete execution state information that mediates between the actual malware runtime behavior and the symbolic execution model, enabling more accurate capability prediction.
3Ease of manufacture
If malware binary is analyzed in sandbox or standalone environment, then code can be dissected, but fileless malware and hardware-locked malware cannot be effectively analyzed
Solution Approach 1:
Instead of analyzing malware by executing it in controlled environments (traditional approach), the system inverts the approach by capturing and analyzing memory artifacts left by malware execution. This allows analysis of fileless malware and hardware-locked malware that cannot be easily dissected in sandbox environments, as the memory images preserve evidence of their behavior.
4Measurement precision
If repeated triage steps are performed manually, then comprehensive malware analysis is achieved, but time is lost and attackers gain advantage
Solution Approach 1:
The system ensures continuity of useful action by automating the entire malware analysis pipeline without interruption. It continuously processes malware samples through disassembly, memory artifact extraction, context reconstruction, and capability identification in an uninterrupted automated workflow, eliminating the time loss associated with manual context switching and repeated triage steps.
Data Source
AI summary
In method of identifying capabilities of a malware intrusion that has been detected by an intrusion detection system, a notification that the malware intrusion has been detected is received from the intrusion detection system. A memory image associated with the malware is then captured. The memory image is parsed and a prior execution context is reconstructed by loading a last central processing unit (CPU) state and memory state into a symbolic environment. Addresses and prototype summaries associated with the malware are extracted from the memory image from the symbolic environment. Paths that are possible for execution due to the malware based on the addresses and prototype summaries are determined. Each path is modeled and a probability of each path being executed with concrete data is assigned. Paths with a low probability of leaving a plurality of paths of interest are pruned. Application programming interfaces (APIs) detected in the plurality of paths of interest are matched to a repository of capability analysis plugins. Any application programming interface (API) that matches at least one plugin in the repository of capability analysis plugins is reported to an analyst.