Silent Malware Detection Feedback Loop
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Solution Overview
Problem
Current antivirus and anti-malware software face challenges in accurately detecting malware, leading to increased false positives and burdensome actions on client machines due to the use of beta and aggressive patterns, which can result in reduced malware detection and user disruption.
Innovation Solution
Implementing a feedback system where client machines silently send malware detection information to a remote in-the-cloud anti-malware service for analysis, reducing intrusive actions on the client and enabling efficient beta pattern testing, while allowing for proactive detection and generation of trial-run solutions to prevent or eliminate malware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beta patterns or aggressive patterns are used by anti-malware software to improve malware detection rate, then detection accuracy is improved, but false positive rate increases and user burden increases
Solution Approach 1:
The patent implements a feedback mechanism where client machines send detection results and file samples back to the anti-malware service. This feedback loop allows the service to validate detections, learn from real-world data, and refine patterns to reduce false positives while maintaining high detection accuracy.
Solution Approach 2:
The patent introduces an intermediary validation layer between pattern matching and user impact. The anti-malware service acts as a mediator that receives feedback from multiple sources, validates detections centrally, and coordinates responses to prevent unnecessary user burden while maintaining security.
2Reliability
If traditional antivirus actions (block, delete, quarantine) are taken when malware is detected, then malware is eliminated, but user productivity decreases due to intrusive actions
Solution Approach 1:
The patent performs preliminary validation and correlation of detection data at the anti-malware service before taking actions on client machines. By pre-validating detections using feedback from multiple sources and applying statistical analysis, the system ensures malware is reliably eliminated while avoiding unnecessary disruptions to user productivity.
3Measurement precision
If manual validation of malware detections is performed to reduce false positives, then detection accuracy is improved, but response time increases
Solution Approach 1:
The patent implements automated validation and correlation systems that perform what would traditionally require manual intervention. The anti-malware service automatically correlates detection data from multiple client machines, applies statistical analysis, and validates patterns without human intervention, maintaining high detection accuracy while enabling rapid response times.
Solution Approach 2:
The patent merges detection data from multiple client machines and multiple detection sources into a centralized validation system. By combining data from numerous sources and applying collective intelligence through correlation analysis, the system achieves high detection accuracy through automated processes that respond quickly to threats.
4Reliability
If extensive testing of beta patterns is performed in client environments to validate safety, then pattern reliability is improved, but testing complexity increases
Solution Approach 1:
The patent creates a universal feedback infrastructure that serves multiple functions: collecting detection data, validating patterns, learning new threats, and coordinating responses. This multi-functional system handles beta pattern testing as part of its overall validation process, reducing testing complexity by integrating it into a broader, coordinated framework rather than requiring separate testing mechanisms.
Data Source
AI summary
Upon detection of a suspicious file, a client computer sends feedback data to an anti-malware service over the Internet. Files that are not suspicious or that are known clean are not reported; files that are known malware are acted upon immediately without needing to report them to the anti-malware service. Upon detection, no alert or warning is provided to the user of the client computer. The anti-malware service correlates data from other detection engines on the client computer or from other client computers and determines whether the file is malware or not. A new virus pattern is generated if the file is malware and includes the virus signature of the file; the new virus pattern is distributed back to the client computers. If not malware, no action need be taken, or, the virus signature of the file is removed from existing pattern files.