Cognitive Malware Task Prediction via Sandbox Analysis
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Solution Overview
Problem
Current methods for identifying malware tasks are largely human-driven, time-consuming, and inefficient, especially when dealing with evolving malware variants, and existing automated solutions struggle with classification due to differences in ground truth and encryption of malware code.
Innovation Solution
The use of a sandbox environment for dynamic analysis, combined with a cognitively inspired learning algorithm, specifically the ACT-R Instance-Based and Rule-Based models, to predict malware tasks by extracting features from analysis reports and leveraging cognitive architectures for more flexible and efficient inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-driven methods are used to identify malware tasks, then classification accuracy may be maintained through expert judgment, but the process becomes extremely time-consuming and inefficient
Solution Approach 1:
The patent replaces human expert analysis (mechanical/cognitive system) with an automated machine learning system that uses cognitive architectures (ACT-R models) to infer malware tasks. The system automatically extracts features from sandbox analysis reports and applies trained models to predict malware capabilities, eliminating the need for manual expert review while maintaining high accuracy through sophisticated algorithms.
Solution Approach 2:
The system enables malware analysis to be self-performing through automated feature extraction and cognitive model inference. The machine learning models are trained on historical data and then autonomously analyze new malware samples without human intervention, allowing the system to serve itself in identifying malware tasks efficiently and consistently.
2Extent of automation
If traditional machine learning techniques are used for malware classification, then automation is achieved, but precision and recall are limited due to differences in ground truth and encrypted malware code
Solution Approach 1:
The patent changes the fundamental parameters of the classification approach by transitioning from traditional supervised learning that relies on predefined malware families and static features to cognitive architectures that model human expert reasoning processes. The ACT-R models incorporate memory, attention, and inference mechanisms that allow the system to handle encrypted code and mutated variants more effectively by focusing on behavioral patterns rather than static code characteristics.
Solution Approach 2:
The system introduces dynamics by using cognitive architectures that can adapt and learn from new data, rather than relying on static classification rules. The ACT-R models dynamically adjust their inference based on extracted features and can handle evolving malware variants by leveraging their learned representations, making the automated classification more robust to changes in malware behavior and encryption methods.
3Extent of automation
If static malware analysis is used to classify malware by family, then automation is achieved for easy samples, but the approach fails when malware code is encrypted or heavily mutated
Solution Approach 1:
The patent transitions from static code analysis to dynamic behavioral analysis by executing malware in sandbox environments and analyzing its runtime behavior. This dimensional shift from examining code structure to observing actual execution patterns allows the system to identify malware tasks even when the code is encrypted or heavily mutated, as the behavioral signatures remain consistent regardless of code obfuscation.
Solution Approach 2:
The system introduces a sandbox environment as an intermediary between the malware and the analysis system. The sandbox acts as a controlled medium where malware can be safely executed and its behavior observed, allowing the cognitive models to infer tasks from behavioral evidence rather than directly analyzing the encrypted or mutated code. This intermediary layer preserves the ability to analyze difficult samples while maintaining safety.
Data Source
AI summary
Embodiments of a system and method for identifying malware tasks using a controlled environment to run malicious software to generate analysis reports, a parser to extract features from the analysis reports and a cognitively inspired learning algorithm to predict tasks associated with the malware are disclosed.