Malware Capability Extraction via API Trigram Sequencing
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Solution Overview
Problem
Current cybersecurity methods struggle to fully extract and analyze the complex, concealed capabilities of sophisticated malware, particularly multipurpose malware, which poses multiple threats simultaneously.
Innovation Solution
A system comprising hardware processors and memory with multiple subsystems, including a malware execution subsystem, a malware activity capturing subsystem, and a malware capability extraction subsystem, which uses trigram sequences, one-hot encoding, and a multi-label deep neural network (DNN) to classify and report on the malignant capabilities of malware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional behavioural analysis is used to provide high-level overview of malware actions, then the analysis process is simple and fast, but it fails to uncover complex malware's concealed capabilities
Solution Approach 1:
The system segments malware analysis into multiple specialized components: static analysis for code structure, dynamic analysis for runtime behavior, and API call sequencing for interaction patterns. Each segment focuses on specific capability indicators, allowing comprehensive detection without overwhelming complexity in a single monolithic system
Solution Approach 2:
The system adds temporal dimension by sequencing API calls in chronological order and analyzing call patterns over time. This transforms static code analysis into dynamic behavioral analysis, revealing concealed capabilities that only manifest during execution through multi-dimensional observation of malware activity
2Measurement precision
If manual analysis is used to understand malware behaviour fully, then comprehensive capability identification is achieved, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system introduces automated analysis engines as intermediaries between raw malware samples and human analysts. These engines perform initial capability extraction, API call sequencing, and behavioral pattern recognition, filtering and organizing data to reduce manual analysis time while maintaining comprehensive capability identification through structured output formats
Solution Approach 2:
Manual analytical processes are replaced with automated computational systems that execute static and dynamic analysis, sequence API calls, and generate capability reports. This substitution maintains high identification accuracy through systematic algorithmic analysis while dramatically increasing throughput by processing multiple samples simultaneously without human intervention
3Quantity of substance
If traditional methods are used to identify multipurpose malware, then dominant capability detection is achieved, but the complete array of malignant capabilities is overlooked
Solution Approach 1:
The system employs a universal analysis framework that simultaneously performs multiple analysis functions: static code scanning, dynamic execution monitoring, API call sequencing, and behavioral pattern recognition. This multi-functional approach detects all malignant capabilities including dominant and concealed ones, as each analysis dimension contributes different capability indicators that together provide complete coverage
Solution Approach 2:
The system analyzes malware across multiple dimensions simultaneously: code structure dimension through static analysis, temporal dimension through API call sequencing, and behavioral dimension through dynamic monitoring. This multi-dimensional approach reveals the complete capability array by observing malware from different analytical perspectives, making concealed capabilities detectable through alternative observation angles
Data Source
AI summary
The present invention discloses a system and method for extracting malware capability. The system comprises a malware execution subsystem configured to execute a malware application in an isolated computing environment, detect one or more changes in system instances and obtain one or more system application programming interface (API) calls and their execution timestamp data from the executed malware application, a malware activity capturing subsystem configured to sort the system API calls based on the obtained timestamp data, generate a trigram sequence from the sorted system API calls, process the trigram sequence to generate one or more feature vectors, a malware capability extraction subsystem configured to classify the received feature vectors based on one or more malignant capabilities of the executed malware and generate a threat report based on analysis of the classified malignant capabilities of the executed malware.