Stack Trace Analysis Model for Malware Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional malware detection techniques are ineffective in identifying and remediating malware code injections, especially in zero-day attacks and when malicious code is injected into trusted processes, as they often rely on static and dynamic analysis, digital signatures, and heuristics, which can lead to false positives and negatives, and fail to address multiple process infections effectively.
Innovation Solution
A system and method using multiple machine learning modules to detect and remediate malware code injections in trusted processes by monitoring thread activities, analyzing execution stacks, and generating verdicts to determine potential malicious behavior, with continuous training on new data to improve detection accuracy and implement remedial actions such as file rollback, registry rollback, and process termination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional malware detection techniques (static and dynamic analysis, digital signatures) are used, then detection capability is provided, but effectiveness deteriorates against modified/obfuscated malware and zero-day attacks
Solution Approach 1:
The system changes the detection parameters from traditional static analysis and digital signatures to dynamic execution stack trace monitoring with machine learning classification. This allows detection to adapt to new malware variants by analyzing behavioral patterns during execution rather than relying on pre-known signatures, thereby improving effectiveness against zero-day attacks and obfuscated code.
Solution Approach 2:
The patent replaces conventional mechanical detection methods (static analysis, signature matching) with a machine learning-based system that monitors execution stacks dynamically. The ML model processes execution trace data in real-time to classify malicious behavior, enabling adaptive detection that can identify new malware types without prior knowledge.
2Productivity
If heuristics-based detection is used, then preliminary detection capability is provided, but final detection accuracy deteriorates due to false positives and negatives
Solution Approach 1:
The system implements feedback mechanisms where the machine learning model continuously processes execution stack traces and updates its classification based on observed patterns. The feedback loop allows the system to learn from detected behaviors and refine its accuracy over time, reducing false positives and negatives while maintaining rapid detection capability.
Solution Approach 2:
The detection system transitions from static heuristic rules to dynamic machine learning classification that adapts to new patterns. The execution stack traces are analyzed in real-time, and the ML model dynamically adjusts its detection criteria based on emerging malware techniques, improving accuracy without sacrificing speed.
3Device complexity
If single-process analysis is performed, then analysis simplicity is maintained, but detection accuracy for multi-process malware deteriorates
Solution Approach 1:
The system implements a universal detection framework that monitors multiple processes simultaneously through their execution stacks. The machine learning model is designed to handle multi-process scenarios, analyzing relationships between processes and identifying coordinated malicious behavior across process boundaries, thereby maintaining simplicity while improving multi-process detection accuracy.
Solution Approach 2:
The patent merges multiple single-process analysis results into a unified multi-process detection framework. By combining execution stack traces from multiple processes and analyzing them through the same ML model, the system achieves accurate detection of multi-process malware while maintaining the simplicity of a single analytical approach.
4Speed
If remediation actions are applied to single processes, then immediate response is achieved, but complete security resolution is not achieved due to remaining malicious processes
Solution Approach 1:
The system performs preliminary monitoring of execution stacks to identify all malicious processes before applying remediation. By detecting and preparing remediation actions for multiple processes simultaneously, the system achieves both rapid response and complete security resolution, as all malicious processes are identified and addressed in the remediation phase.
Data Source
AI summary
Systems and methods for detecting a malware injection interested processes. The method includes identifying one or more trusted processes, monitoring at least one thread associated with the trusted processes using at least one control point, detecting activity at the at least one thread based on the at least one control point and determining a timestamp of the detected activity, receiving from the trusted processes at least one execution stack corresponding to the timestamp and indicating the at least one control point used to monitor the at least one thread, applying a first malware detector to the at least one execution stack to generate a first verdict, collecting the first verdict and auxiliary information corresponding to the trusted processes at the given timestamp, and applying a second malware detector to the first verdict and the auxiliary information to generate a second verdict.