Machine Learning Ransomware Detection via Feature Probability
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Solution Overview
Problem
Current methods fail to effectively detect and prevent ransomware attacks, which can disrupt computer and network operations, steal data, and demand ransom payments, as they rely on traditional signature-based detection that is inadequate against evolving ransomware threats.
Innovation Solution
A computer-implemented method using a machine-learning model with multiple layers of logic to analyze source code for ransomware by extracting features related to file operations, cryptography, and data communication, determining probabilities, and flagging scripts that exceed a threshold, thereby blocking execution and blacklisting malicious servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signature-based detection methods are used, then the detection process is simple and fast, but the detection accuracy is insufficient against evolving ransomware threats
Solution Approach 1:
The patent replaces traditional signature-based detection (mechanical rule-based system) with a machine learning model that uses probabilistic analysis of code features. The system extracts features from source code and applies a machine learning model with multiple layers of logic to determine the probability of ransomware, substituting deterministic signature matching with probabilistic pattern recognition that adapts to evolving threats.
2Measurement precision
If machine learning models with multiple features are used, then detection accuracy improves, but the processing time and computational complexity increase
Solution Approach 1:
The patent segments the source code into distinct features representing different functional categories (file operations, cryptography, data communication, etc.). By dividing the code analysis into discrete feature extraction and evaluation steps, the system can process complex multi-feature machine learning models in a structured manner, improving both accuracy and processing efficiency.
Solution Approach 2:
The system performs preliminary feature extraction from the source code before applying the machine learning model. By pre-processing the code to identify and extract relevant features (keywords, code structures, operational patterns) beforehand, the system reduces the computational burden during the actual detection phase, thereby decreasing overall processing time while maintaining high detection accuracy.
3Reliability
If comprehensive feature extraction and probability analysis are performed, then the reliability of detection increases, but the ease of operation decreases
Solution Approach 1:
The machine learning model automatically performs feature extraction, probability calculation, and detection decision-making without requiring manual intervention. The system self-adjusts by learning from training data and automatically determines the probability that extracted features indicate ransomware, eliminating the need for operators to manually analyze complex features or tune detection parameters, thereby maintaining high reliability while improving ease of operation.
Data Source
AI summary
Implementations can provide a method that includes: accessing the source code of a script hosted by a remote server; extracting features from the source code in accordance with a machine-learning model comprising one or more layers of logic; at least based on the machine-learning model, determining, for each of the extracted features, a corresponding probability conditioned on the source code containing ransomware; and at least based on the machine-learning model, determining a combined probability for the extracted features conditioned on the source code containing ransomware when the extracted features are jointly present; comparing the combined probability with a threshold; in response to determining that the combined probability exceeds the threshold, flagging the source code as containing ransomware; and in response to determining that the combined probability does not exceed the threshold, flagging the source code as not containing ransomware.