Byte Frequency Distribution Analysis for Ransomware Detection
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Solution Overview
Problem
Existing ransomware detection methods, particularly entropy-based approaches, struggle with early detection of malicious encryption due to low encryption levels, false positives from compressed files, and inability to differentiate between legitimate and malicious encryption, leading to inefficient and inaccurate threat response.
Innovation Solution
A machine learning model, such as a transformer model, analyzes byte frequency distributions of data samples to classify encrypted and non-encrypted portions, using feature vectors to detect ransomware attacks by monitoring the share of encrypted data exceeding a threshold, thereby initiating mitigative actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entropy-based methods are used for ransomware detection, then detection capability is provided, but false positives occur due to compressed files and low encryption levels
Solution Approach 1:
The patent transitions from using entropy as the detection parameter to using byte frequency distribution as the new parameter. This change allows the system to capture more nuanced characteristics of encrypted data, distinguishing between legitimately compressed files and ransomware-encrypted files by analyzing the distribution patterns of byte values rather than relying solely on entropy measurements.
Solution Approach 2:
The patent replaces traditional entropy-based detection mechanisms with a machine learning model that processes byte frequency distribution data. This substitution enables the system to learn complex patterns and relationships in the data that simple entropy calculations cannot capture, thereby reducing false positives while maintaining detection accuracy.
2Loss of time
If traditional detection methods are used, then processing overhead is manageable, but early detection capability is insufficient due to low encryption levels
Solution Approach 1:
The system performs preliminary analysis of byte frequency distributions on data blocks as they are being processed or stored, before ransomware can complete its encryption cycle. By establishing baseline frequency distributions and continuously monitoring for deviations, the system can detect early signs of encryption activity before the attack fully manifests, enabling timely intervention.
Solution Approach 2:
The patent implements continuous monitoring of byte frequency distributions across data blocks, creating an ongoing detection process rather than periodic checks. This continuous analysis allows the system to detect encryption activities at any stage, maintaining constant surveillance for anomalies in the frequency distribution patterns that would indicate ransomware execution.
3Measurement precision
If byte frequency distribution analysis is implemented, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent divides the data into discrete blocks and calculates byte frequency distributions for each block independently. This segmentation approach allows the system to process data in manageable units, reducing the computational burden compared to analyzing entire files at once. The block-level analysis also enables parallel processing, further mitigating the complexity increase.
Solution Approach 2:
The patent introduces feature vectors as an intermediary representation between the raw byte frequency distribution data and the machine learning model. These feature vectors condense the essential characteristics of the frequency distributions into a more compact form, reducing the dimensionality and computational complexity while preserving the information needed for accurate encryption detection.
Data Source
AI summary
Systems, methods, and software are disclosed herein for detecting encrypted data in various implementations. In an implementation, a computing apparatus determines byte frequency distribution values associated with a compute workload. The computing apparatus executes a machine learning model trained to differentiate between encrypted portions and non-encrypted portions of the compute workload based on the byte frequency distribution values. The computing apparatus monitors an encrypted share of the compute workload represented by the encrypted portions and, in response to the encrypted share meeting or exceeding a threshold, initiating a mitigative action.