Ransomware Index Categorization Using Autoencoder Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Ransomware attacks encrypt data to extort victims, often targeting valuable or sensitive information, and existing methods lack effective means to categorize and respond appropriately to different ransomware variants.
Innovation Solution
A method using an autoencoder trained on searchable encryption indices to identify ransomware algorithms, enabling responsive actions to mitigate their effects, and a system to decrypt encrypted data using identified algorithms and seed parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If ransomware encrypts data to extort victims, then the attacker gains control over encrypted data, but the victim loses access to their data and systems become compromised
Solution Approach 1:
The system performs preliminary actions by exposing the target computer system to the ransomware algorithm in a controlled manner before actual attacks occur, capturing encryption indices during this exposure phase to build training data for the autoencoder model
Solution Approach 2:
The system converts the harmful encryption indices generated by ransomware into beneficial training data for the autoencoder model. By using the ransomware's own encryption behavior as training material, the system transforms the threat into a detection capability
2Adaptability or versatility
If traditional methods are used to detect ransomware, then detection capability is limited, but the system cannot effectively categorize different ransomware variants
Solution Approach 1:
The autoencoder model serves as an intermediary between raw encryption indices and ransomware algorithm identification. The model translates complex encryption patterns into categorized classifications, enabling precise identification of ransomware variants
Solution Approach 2:
The system changes parameters by training the autoencoder on multiple encryption indices from different ransomware algorithms, allowing the model to learn and distinguish between various encryption patterns and adapt to different ransomware types
3Measurement precision
If the system exposes target computer systems to ransomware algorithms for training, then detection accuracy improves, but security risks increase during the training phase
Solution Approach 1:
The system performs preliminary exposure to ransomware in a controlled training environment before deployment, capturing encryption indices beforehand to build the autoencoder model without exposing production systems to actual attack risks
Solution Approach 2:
The system uses copies of encryption indices from ransomware algorithms as training data instead of exposing live systems to actual attacks. The autoencoder is trained on replicated encryption patterns rather than real-world attack scenarios
Data Source
AI summary
A computer implemented method of identifying a ransomware algorithm, the ransomware algorithm having associated a predetermined responsive action for mitigating the effect of the ransomware algorithm in use, the method including exposing a target computer system to the ransomware algorithm, the target computer system containing a predetermined set of sample data stored therein that is encrypted by the ransomware algorithm using a searchable encryption algorithm; intercepting an index of the searchable encryption algorithm; training an autoencoder based on the index to provide a trained autoencoder adapted to identify the ransomware algorithm based on the index.