Malware Vector Detection via Human Interface Device Input Analysis
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Solution Overview
Problem
The growing threat of malicious software, or malware, which can compromise computer systems by presenting as human interface devices and generating inputs that mimic human behavior, poses a challenge in identifying and mitigating potential malware threat vectors.
Innovation Solution
A computer-implemented method and system that identifies computing systems with existing human interface devices, detects new devices presenting as similar, and determines if they generate input events equivalent to the existing devices, analyzing these inputs for attributes like frequency, timing, and precision to identify potential malware attack vectors, and optionally blocks or prompts the user to allow input from such devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the computing system accepts inputs from new human interface devices, then the system maintains compatibility and ease of operation, but the system becomes vulnerable to malware attacks
Solution Approach 1:
The system performs preliminary analysis of input patterns from new human interface devices before fully accepting their inputs. By establishing baseline expectations for human-generated inputs and comparing new device inputs against these baselines, the system proactively identifies potential malware devices before they can execute harmful actions, thus preventing attacks while maintaining compatibility with legitimate devices
Solution Approach 2:
The system introduces an intermediary analysis layer between the human interface device and the computing system core. This intermediary monitors and analyzes input patterns, acting as a mediator that allows legitimate device inputs to pass through while blocking malicious inputs, thus resolving the contradiction between accepting diverse devices and preventing malware
2Difficulty of detecting and measuring
If the system analyzes input patterns to identify malware, then security detection capability improves, but system complexity increases
Solution Approach 1:
The system focuses analysis on specific key parameters of input patterns such as frequency, timing, and precision rather than analyzing all possible device characteristics. By changing the parameters under analysis to only the most discriminative ones, the system achieves effective malware detection while keeping the analysis process manageable and avoiding excessive system complexity
Solution Approach 2:
The system applies different analysis depths to different input scenarios. For example, it may apply more stringent analysis to high-risk input types while using simpler validation for low-risk inputs. This localized quality approach allows the system to maintain high detection capability where needed while avoiding unnecessary complexity in other areas
3Reliability
If the system blocks inputs from suspicious devices, then security improves, but loss of legitimate inputs increases
Solution Approach 1:
The system implements feedback mechanisms where input blocking decisions can be reviewed and adjusted. By monitoring the effects of blocking decisions and allowing for correction of false positives, the system maintains high security while minimizing loss of legitimate inputs through continuous learning and adaptation
Data Source
AI summary
A computer-implemented method for identifying malware threat vectors may include 1) identifying a computing system that includes a first human interface device, 2) detecting an introduction of a new device to the computing system that presents itself to the computing system as a second human interface device, 3) determining that the second human interface device is configured to generate a type of input event equivalent to the type of input event generated by the first human interface device, 4) determining, based on the second human interface device being configured to generate the type of input event equivalent to the type of input event generated by the first human interface device, that the second human interface device includes a potential malware attack vector. Various other methods, systems, and computer-readable media are also disclosed.