Ransomware Encryption Algorithm Identification via Autoencoder
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Solution Overview
Problem
Ransomware attacks encrypt data, making it difficult to distinguish between different encryption algorithms used, which complicates defensive and responsive measures, as each ransomware has unique characteristics that require specific reactive strategies.
Innovation Solution
A method involving intercepting messages from a target computer system to a ransomware server, inspecting padding bytes, and training an autoencoder to differentiate between encryption algorithms used by ransomware and other algorithms, allowing for identification and appropriate responsive actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ransomware uses unique encryption algorithms with different characteristics, then the security and versatility of the ransomware is improved, but the difficulty of detecting and measuring the encryption algorithm increases
Solution Approach 1:
The patent applies color changes by detecting specific byte values (analogous to color indicators) within the encrypted data to identify encryption algorithms. Different encryption algorithms leave distinct byte patterns that serve as detectable signatures, allowing the system to distinguish between various encryption methods despite their unique characteristics.
Solution Approach 2:
The patent replaces manual analysis of encryption algorithms with an automated machine learning system. The autoencoder neural network processes encrypted data blocks and automatically identifies encryption algorithms based on their structural patterns, substituting complex manual detection mechanisms with an automated computational approach.
2Measurement precision
If the system analyzes detailed encryption patterns to identify algorithms, then the measurement precision of encryption algorithm identification is improved, but the device complexity increases
Solution Approach 1:
The patent extracts only the most critical identifying features from the encrypted data - specifically certain byte values and their patterns - for analysis by the autoencoder. This extraction approach allows the system to achieve high identification accuracy by focusing on key diagnostic elements rather than analyzing every detail of the encrypted data, thereby reducing processing complexity.
Solution Approach 2:
The patent uses a trained autoencoder model that replicates the patterns of known encryption algorithms during training. During operation, the model generates responses that mirror the structural characteristics of specific encryption algorithms, enabling accurate identification without requiring complex real-time analysis of the original encrypted data.
3Reliability
If the system processes all encrypted data to identify patterns, then the reliability of encryption algorithm detection is improved, but the loss of time increases
Solution Approach 1:
The patent segments the encrypted data into discrete blocks and analyzes only specific portions (particular byte values and patterns) within those blocks for identification purposes. This segmentation allows the system to maintain high detection reliability by examining representative samples while avoiding the time-consuming task of processing every byte of encrypted data in detail.
Solution Approach 2:
The patent performs preliminary action by pre-training the autoencoder model on known encryption algorithms before actual detection occurs. During training, the model learns and stores the characteristic patterns of various encryption algorithms. When detection is needed, the pre-trained model can quickly identify algorithms by comparing current data against its learned patterns, significantly reducing real-time analysis time while maintaining high reliability.
Data Source
AI summary
A computer implemented method of identifying an encryption algorithm used by a ransomware algorithm, the ransomware algorithm encrypting a data store of a target computer system using a searchable encryption algorithm, the method including intercepting an ordered plurality of messages communicated from the target computer system to a ransomware server computer system, each message including a payload storing an encrypted unit of data from the target computer system; inspecting a final byte in the encrypted unit of data in each message to identify a byte value used by an encryption algorithm of the ransomware as a padding byte to pad messages to the size of an integral multiple of units of encryption for the encryption algorithm; training an autoencoder based on a position of a message in the ordered plurality of messages and the padding byte to provide a trained autoencoder adapted to differentiate the encryption algorithm used by the ransomware from other different encryption algorithms.