Conference Bot Detection Using Behavioral Attendee Analysis
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Solution Overview
Problem
Existing web conference platforms lack effective methods to distinguish between human participants and AI bots, exposing them to potential security risks such as data exfiltration and unauthorized access.
Innovation Solution
Implementing a system that monitors web conferences using a combination of web hooks and APIs to collect attendee attributes, applies machine learning models and heuristic models to determine bot presence, and takes actions such as removal or alerting based on behavioral analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If bots are allowed to join web conferences freely for harmless purposes like note-taking, then ease of operation is improved, but security reliability deteriorates due to expanded attack surface for data exfiltration
Solution Approach 1:
The patent introduces an intermediary bot detection system that sits between the web conference platform and incoming attendees. This system analyzes attendee attributes and behavior patterns to determine whether to allow access, thereby mediating between the need for open access and security requirements. The intermediary validates each attendee without preventing legitimate bot functionality.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring attendee behavior patterns and adjusting access decisions based on detected anomalies. When suspicious patterns are detected, the system provides feedback to block or alert on specific attendees while maintaining access for normal participants, creating a dynamic security response.
2Device complexity
If traditional monitoring methods are used to identify bot attendees, then device complexity is reduced, but measurement precision deteriorates leading to high false positive rates
Solution Approach 1:
The monitoring system is segmented into multiple independent analysis components, each evaluating specific attendee attributes such as join timing, interaction patterns, audio characteristics, and metadata. This segmentation allows complex detection logic to be distributed across simple, specialized modules, maintaining low overall system complexity while achieving high detection precision through cumulative analysis.
Solution Approach 2:
The system changes detection parameters dynamically by adjusting the weight and threshold of various attendee attributes based on contextual factors. Different meeting types, organizational policies, and detected threat patterns cause parameter adjustments that optimize detection accuracy without requiring complex reconfiguration of the underlying system structure.
Data Source
AI summary
One or more attendees joining a web conference are monitored. It is determined that one or more of the one or more attendees is an undesired attendee. In response to the determination, an action is caused to be performed.