AI Ransomware Detection Using File Profile Analysis
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Solution Overview
Problem
Existing methods for detecting ransomware attacks are not reliable and can fail to identify the malware until irreversible damage occurs, as they rely on signatures, network monitoring, or backup integrity, which may also be compromised.
Innovation Solution
A machine learning-based system trained on curated data profiles from ransomware samples, using xgboost algorithm for detection, analyzes file metadata and content to predict ransomware attacks with high confidence, avoiding reliance on continuous updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional signature-based scanning or network monitoring methods are used to detect ransomware, then the detection system is simple to implement and maintain, but the reliability of detection is low and attacks may go undetected until irreversible damage occurs
Solution Approach 1:
The patent replaces traditional mechanical signature-matching detection methods with an AI-based system that uses machine learning models to analyze file statistical profiles. The system substitutes rule-based scanning with intelligent algorithms that can identify ransomware behavior patterns without relying on pre-defined signatures, thereby improving detection reliability while maintaining operational simplicity through automated analysis.
Solution Approach 2:
The invention changes the detection parameters from static signature matching to dynamic statistical profile analysis. By monitoring changes in file attributes, metadata, and behavioral patterns over time, the system detects ransomware based on parameter deviations rather than fixed signatures. This allows the system to adapt to new ransomware variants without requiring updates to detection rules.
2Reliability
If signature-based detection with updated ransomware libraries is used, then detection accuracy improves for known threats, but the system fails to detect new ransomware variants and requires continuous updates
Solution Approach 1:
The patent implements a dynamic detection system that adapts to new ransomware variants through continuous learning. The AI model analyzes statistical profiles of files and adjusts its detection parameters based on observed patterns, enabling it to identify both known and emerging threats. This dynamic approach replaces static signature databases with an adaptable intelligent system that evolves with new ransomware techniques.
Solution Approach 2:
The system performs preliminary analysis of file statistical profiles and metadata before ransomware execution completes. By establishing baseline characteristics of legitimate files and comparing them against observed changes, the system detects anomalies early in the infection process. This preliminary detection capability allows identification of new ransomware variants before they can encrypt critical data, without requiring prior knowledge of specific threat signatures.
3Reliability
If network-level monitoring is implemented to identify unusual traffic patterns, then detection coverage is expanded, but false positives increase and detection speed decreases
Solution Approach 1:
The patent extracts detection analysis from the network layer and moves it to the local file system level. By analyzing statistical profiles of individual files and their metadata directly at the source, the system eliminates the need for extensive network traffic monitoring. This extraction of the detection function to where the actual ransomware activity occurs enables faster, more accurate detection with minimal false positives, as the analysis focuses on local file changes rather than indirect network indicators.
Data Source
AI summary
A method of causing ransomware to be executed includes obtaining a set of ransomware samples and automatically performing a set of scripted detonations of a first subset of the ransomware samples according to a schedule. The set of scripted detonations includes executing the first subset of ransomware samples on a virtual machine. During the execution of the first subset of ransomware samples, use of computing resources by the first subset of ransomware samples is monitored until a shutdown threshold is reached. Upon reaching the shutdown threshold, any pending detonation tasks of the execution of the first subset of ransomware samples are purged. Detonation-infected disk images are generated to include files resulting from the execution of the first subset of ransomware samples, and the detonation-infected disk images are used in training a machine learning system to assess a likelihood of a presence of a ransomware attack.