Rootkit Detection via Memory Dump Forensic Analysis
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Solution Overview
Problem
Current malware detection systems, particularly those targeting rootkits, face challenges in efficiently detecting unknown variants and require extensive manual analysis, which is time-consuming, resource-intensive, and prone to errors due to the use of obfuscation techniques.
Innovation Solution
A computer security system utilizing a machine learning model trained on memory dumps and system data to identify suspicious patterns, allowing for automated forensic analysis and reducing the need for manual examination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based anti-malware systems are used to detect rootkits, then known malware can be identified, but previously unknown variants and new malware cannot be detected
Solution Approach 1:
The system performs preliminary actions by collecting extensive system data including memory dumps, network logs, file system data, and registry information before detection is needed. This pre-collected data forms a comprehensive baseline that enables the machine learning model to identify both known and unknown malware variants through pattern recognition rather than signature matching.
Solution Approach 2:
The patent replaces the mechanical signature-based detection system with a machine learning-based automated analysis system. The ML model processes raw system data directly, eliminating the need for manual signature creation and updating, thereby automatically adapting to new malware variants without requiring reconfiguration or manual intervention.
2Measurement precision
If manual forensic analysis is performed to identify rootkits, then detailed analysis can be conducted, but it requires high skills, time and resource expenditure
Solution Approach 1:
The system implements self-service by automating the entire forensic analysis process through machine learning. The ML model independently collects data, processes memory dumps, analyzes system artifacts, and generates detection results without requiring skilled analysts. This automation maintains high detection accuracy while eliminating the time and resource costs associated with manual analysis.
Solution Approach 2:
The patent extracts and isolates only the most relevant suspicious data blocks from the vast amount of collected system data using the machine learning model. This extraction process focuses analysis on critical areas, maintaining measurement precision by identifying truly suspicious elements while significantly reducing the time required compared to comprehensive manual examination of all system data.
3Reliability
If memory dumps are collected and analyzed to detect rootkits, then rootkit presence can be determined, but large amounts of memory storage are required and analysis becomes more difficult
Solution Approach 1:
The system segments the large memory dump data into manageable components and processes them through the machine learning model. The ML algorithm divides the analysis task into identifying and processing only suspicious data blocks, rather than analyzing the entire memory dump. This segmentation reduces storage requirements and simplifies analysis complexity while maintaining reliable detection capability.
Solution Approach 2:
The patent changes the parameter of data representation by using machine learning models to process raw memory dump data in transformed representations. The ML model converts complex binary memory data into recognizable patterns and features, changing the parameter space from raw bytes to meaningful characteristics that are easier to analyze and require less storage while preserving detection reliability.
4Reliability
If forensic analysis is performed on entire system dumps, then comprehensive coverage is achieved, but it requires processing large volumes of data increasing time and resource requirements
Solution Approach 1:
The system applies partial action by using the machine learning model to identify and analyze only the suspicious portions of system data rather than processing entire system dumps. The ML model determines which data blocks require forensic analysis, performing partial examination that maintains detection thoroughness for critical areas while significantly improving productivity by avoiding unnecessary processing of clean data.
Data Source
AI summary
Forensic analysis on consistent system footprints relates to a system and method for rootkit detection based on forensic analysis performed on consistent system footprints, such as application events, application network communications and application files. The system includes a security system periodically monitoring one or more applications of a computing system. The security system includes a threat detection unit for collecting and storing system memory dumps, a machine learning module trained on clean and infectious memory dump, a similarity scanner to identify similarity between suspicious memory block and consistent system footprints, and a forensic analyzer to perform forensic analysis and detect infection, if any, based on the similarity found. The suspicious memory block is identified by the threat detection unit based on the analysis performed by the machine learning model. Upon rootkit detection an alert and forensic analysis report are generated.