Contextual Feedback Triangulation for Malicious Content Detection
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Solution Overview
Problem
Primary content providers face challenges in detecting and isolating malicious third-party content, such as malware, within their platforms due to lack of control over supplemental content from advertisers or other providers, leading to potential revenue loss and user experience degradation when issues are discovered only after user complaints.
Innovation Solution
Implementing a system that collects and analyzes user feedback with additional identifying information, allowing for the triangulation of problematic content sources by correlating user reports with session and log data, enabling swift identification and action against malicious sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If primary content providers include supplemental content from third-party providers (advertisers, content partners), then the variety and quantity of content provided to users increases, but the primary provider loses control over the specific content and cannot detect malicious code before it is loaded onto user devices
Solution Approach 1:
The patent implements a feedback mechanism that proactively collects and analyzes information about third-party content before it is fully loaded or executed on user devices. The system pre-detects malicious code by monitoring feedback signals from user devices, browser console errors, and content behavior patterns, allowing the primary content provider to identify and block harmful content before it can compromise user security. This preliminary detection resolves the contradiction by maintaining content variety while ensuring safety through advance validation.
2Device complexity
If the primary content provider waits for user complaints to detect problematic third-party content, then the detection process is simple, but the provider experiences significant revenue loss and user experience degradation while advertising is restricted
Solution Approach 1:
The patent implements a comprehensive feedback mechanism that continuously monitors multiple data sources including user device feedback, browser console errors, content behavior patterns, and performance metrics. This feedback loop enables the system to automatically detect malicious content in real-time without waiting for user complaints. The feedback system processes signals from various sources, correlates them with session data, and triggers automated responses to block problematic content, thereby reducing detection time and minimizing revenue loss while maintaining a manageable system complexity through automated analysis.
3Reliability
If the primary content provider implements comprehensive monitoring of all third-party content, then malicious content can be detected quickly, but the system complexity and resource requirements increase significantly
Solution Approach 1:
The patent implements a targeted monitoring approach that focuses computational resources on specific high-risk indicators rather than uniformly monitoring all third-party content. The system prioritizes monitoring of feedback signals from user devices, browser console errors, and specific content behavior patterns that are most indicative of malicious activity. By applying different levels of monitoring intensity to different content sources and types based on their risk profiles, the system achieves comprehensive malicious content detection capability while managing system complexity and resource requirements through selective, localized monitoring strategies.
Data Source
AI summary
Feedback received from users regarding potential problems with an application, service, or other source of electronic content can be configured to include additional information that help triangulate the source of the problem. Content provided by third parties can be combined with content from a primary provider, but the primary provider often will be unable to determine the precise instance of third party content that a user received that might have posed a problem for the user, as may relate to malware or another such issue. By configuring feedback submissions from users to automatically include identifying information, and by logging session data for various users, a content provider can analyze and filter the data to determine likely sources of the problem, and deactivate or otherwise address those sources. Further, the content provider can analyze the information to locate any users likely to have been exposed to the same third party content.