Rootkit Detection via Differential System Dump Analysis
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Solution Overview
Problem
Current rootkit detection methods, particularly those using heuristic and signature-based techniques, face limitations in precision and efficiency, and existing memory dump analysis techniques require significant memory storage and struggle with accurate classification, making it difficult to detect and prevent kernel-level rootkit infections effectively.
Innovation Solution
A computer security system that utilizes a system dump sequence analysis, comprising a system event monitor, a system dump capture driver, and a rootkit detection engine with a machine learning model to classify system states as infected, suspicious, or clean by analyzing differential memory dumps and application activity events, optimizing detection and minimizing unnecessary memory processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If heuristic detection techniques are used to detect rootkits by detecting suspicious behavior, then detection capability is improved, but precision and classification accuracy deteriorate
Solution Approach 1:
The patent replaces traditional heuristic detection methods with machine learning-based classification. The system uses trained machine learning models to analyze system dumps and automatically classify rootkit types, replacing manual or rule-based heuristic analysis with automated intelligent classification, thereby improving both detection capability and precision simultaneously
Solution Approach 2:
The patent changes the detection parameters from simple suspicious behavior indicators to comprehensive system dump analysis featuring multiple characteristics. By analyzing numerous parameters in system dumps through machine learning models, the system achieves more accurate rootkit classification while maintaining high detection capability
2Difficulty of detecting and measuring
If signature-based techniques are used for detecting rootkits, then detection capability is improved, but efficiency and processing cost deteriorate
Solution Approach 1:
The patent extracts and analyzes only the necessary features from system dumps that are relevant to rootkit detection, rather than processing entire system images or all memory contents. This selective extraction approach maintains high detection capability while significantly improving processing efficiency and reducing computational costs
Solution Approach 2:
The patent segments the detection process into distinct stages: system dump capture, feature extraction, machine learning classification, and result interpretation. This segmentation allows each component to be optimized independently, improving overall detection efficiency while maintaining capability
3Measurement precision
If memory dump analysis is performed to detect rootkits, then detection precision is improved, but memory storage requirements and system performance degradation worsen
Solution Approach 1:
The patent uses temporary, disposable system dumps that are captured, analyzed, and then discarded. These dumps are short-lived intermediate files stored in memory during the detection process, requiring minimal storage space compared to permanent backup systems, while still enabling high-precision analysis
Solution Approach 2:
The patent performs partial analysis of system dumps by focusing only on relevant memory regions and features rather than analyzing entire system images. This partial action approach reduces memory storage requirements and processing overhead while maintaining detection precision through targeted analysis of critical areas
Data Source
AI summary
The present disclosure relates to a system and method for rootkit detection based on a system dump sequence analysis. The system includes a security system in communication with one or more applications of a computing system. The security system includes a system event monitor to monitor events occurring at the applications, a system dump capture driver to capture differential system dumps corresponding to each event, and a rootkit detection engine to determine if a system state is infected. The rootkit detection engine is based on a machine learning model, where the machine learning model is trained on collection of clean system dumps and infectious system dumps. Based on analysis carried out by the machine learning model, the rootkit detection engine can classify the system state as suspicious, infectious, or clean state.