Ransomware Seed Parameter Detection via API Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Ransomware attacks encrypt data, making it inaccessible unless a payment is made, and attackers often selectively decrypt valuable information for extortion, posing challenges in identifying and responding to the specific characteristics of different ransomware variants.
Innovation Solution
A computer-implemented method that exposes a target computer system to a ransomware algorithm, monitors API calls to identify hardware component data as seed parameters for generating an encryption key, and uses autoencoders to recognize and respond to specific ransomware algorithms, enabling effective defensive measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ransomware algorithms generate encryption keys using hardware component data as seed parameters, then the encryption strength and uniqueness of each target system is improved, but the complexity of detecting and identifying specific ransomware variants increases
Solution Approach 1:
The system performs preliminary monitoring of API calls to capture hardware component data before encryption occurs. By recording the seed parameters (CPU info, memory details, storage configuration, etc.) in advance and storing them in a database, the system enables later identification of ransomware variants without interfering with the encryption process itself.
Solution Approach 2:
The patent introduces an intermediary database that stores captured hardware component data and seed parameters. This database acts as a mediator between the ransomware algorithm and the detection system, allowing the system to compare captured data against stored profiles to identify ransomware variants without directly analyzing the encryption process.
2Measurement precision
If the system monitors all API calls to identify seed parameters, then the accuracy of ransomware detection is improved, but the computational resources and monitoring complexity increase
Solution Approach 1:
The monitoring system segments API calls into specific categories related to hardware components (CPU, memory, storage, etc.). Instead of monitoring all possible API calls, the system focuses on identifying and capturing only the relevant seed parameter data from hardware component queries, reducing monitoring overhead while maintaining detection accuracy.
Solution Approach 2:
The system applies local quality by capturing only the specific attributes of hardware components that serve as seed parameters (such as serial numbers, version information, and configuration data) rather than monitoring all hardware details. This selective approach reduces complexity while maintaining the precision needed for accurate ransomware identification.
Data Source
AI summary
A computer implemented method for determining a plurality of data sources providing seed parameters for generation of an encryption key by a ransomware algorithm, the method including exposing a target computer system to the ransomware algorithm; monitoring application programming interface (API) calls made to an operating system of the target computer system to identify a set of API calls for retrieving data about one or more hardware components of the target computer system, the data about the hardware components being determined to constitute the seed parameters.