Ransom Note Classification in Received Files for Early Ransomware Detection
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Solution Overview
Problem
Existing ransomware detection systems are slow to detect malicious code, allowing it to cause damage before proper classification, and lack effective methods to identify ransom notes quickly.
Innovation Solution
A framework that utilizes sentiment analysis on tokenized and lemmatized text data to classify files as ransomware by generating a score based on vectorized features, suspending the process if the score exceeds a threshold, and alerting the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static classification and runtime analysis are used to detect ransomware, then detection accuracy is improved, but detection speed deteriorates allowing malicious code to cause damage before classification
Solution Approach 1:
The patent applies preliminary action by extracting and analyzing text content from files before they are executed. The system creates a text representation of file contents, extracts textual features, and classifies them as ransomware-related before the malicious code can cause harm. This pre-execution analysis enables early detection and neutralization of ransomware threats.
Solution Approach 2:
The patent applies extraction by separating text content analysis from traditional executable code analysis. The system extracts text representations from files, isolates textual features, and analyzes them independently using NLP techniques. This extraction approach enables rapid classification of ransomware based on characteristic text patterns without requiring full runtime execution.
2Reliability
If traditional ransomware detection systems wait for multiple file change events to occur, then they can confirm malicious behavior, but by then malicious code has already caused damage
Solution Approach 1:
The system performs preliminary classification of files based on their text content before multiple file change events occur. By analyzing textual features and generating ransomware probability scores early in the file lifecycle, the system can neutralize threats before they propagate across the network or cause extensive damage.
Solution Approach 2:
The patent introduces text content analysis as an intermediary between file reception and full execution. This intermediary layer provides early warning signals through text-based classification, allowing the system to intervene before the malicious code completes its harmful actions. The text analysis acts as a mediator that enables early detection without requiring confirmation through multiple file change events.
3Measurement precision
If sentiment analysis with tokenization and lemmatization is applied to classify files, then ransomware identification accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent replaces traditional mechanical code analysis methods with natural language processing techniques. Instead of analyzing binary executable code through complex disassembly and debugging processes, the system substitutes text-based NLP methods including tokenization, lemmatization, and sentiment analysis. This substitution simplifies the analysis process while maintaining high accuracy in identifying ransomware characteristic text patterns.
Solution Approach 2:
The patent applies parameter changes by transforming raw text data into standardized NLP features through tokenization and lemmatization. These parameter transformations convert unstructured text into structured numerical representations that can be efficiently processed by classification algorithms. The parameter changes enable the system to handle linguistic variations while maintaining consistent classification accuracy.
Data Source
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AI summary
The present invention analyzes the text of a received file to determine if the file likely is a forensic artifact of a ransomware attack on a computer system. If the computer system concludes that the file is likely an artifact of a ransomware attack, the system terminates or ignores all related processes, thereby minimizing the harm caused to the computer system.