Malware Detection via Sensor Behavioral Analysis
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Solution Overview
Problem
Existing methods for detecting ransomware, especially zero-day attacks, are ineffective due to the lack of known data signatures and the increasing presence of polymorphic malware, making signature-based approaches less reliable.
Innovation Solution
A system and method that utilizes data from multiple sensors in a computing device to analyze behavior patterns using predictive models, allowing for the detection of malware initiation without relying on direct signature recognition, by monitoring side-channel information such as temperature, power consumption, and other sensor data to identify suspicious behavior indicative of encryption operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based detection methods are used, then detection accuracy for known malware is improved, but detection capability for zero-day attacks deteriorates
Solution Approach 1:
The patent transforms the detection approach by changing from signature-based parameters to sensor-based behavioral parameters. Multiple sensors (temperature, acoustic, power consumption, etc.) monitor physical parameters that change during malware execution, enabling detection of zero-day attacks through anomalous behavioral patterns rather than known signatures.
Solution Approach 2:
The patent introduces sensor data as an intermediary between malware execution and detection. Instead of directly analyzing malware code or signatures, the system uses sensor readings (temperature changes, acoustic emissions, power consumption patterns) as intermediate indicators that reflect malware behavior, thereby detecting unknown threats indirectly through their physical effects.
2Reliability
If multiple sensors are deployed to improve detection accuracy, then system complexity increases
Solution Approach 1:
The patent segments the detection system into independent sensor modules, each monitoring specific physical parameters (temperature, acoustic, power). This modular segmentation allows the system to achieve high detection reliability through multiple data sources while managing complexity by treating each sensor as an independent unit with standardized processing pipelines.
Solution Approach 2:
The patent creates a universal detection framework that processes data from multiple different sensor types through a common analysis pipeline. The sensor fusion architecture and machine learning models are designed to handle diverse sensor inputs uniformly, enabling the system to achieve multi-functional detection capability without proportionally increasing operational complexity.
Data Source
AI summary
In some implementations, a method includes retrieving data from multiple sensors in a computing device, and the multiple sensors comprise different types of sensors. The sensor data is analyzed based on a predictive model, and the predictive model is trained to detect malware. Initiation of malware is determined based on the analysis. In response to the determination, the malware is terminated.