Survey Bot Detection Using Adaptive Multi-Stage Challenges
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Solution Overview
Problem
Automated computer programs (bots) are used to manipulate survey platforms, leading to economic loss for clients paying for legitimate responses and earning rewards without providing authentic input, necessitating a system to detect and eliminate such bots.
Innovation Solution
A bot detection system that assigns users to classifications, provides challenges over multiple instances, monitors user behavior, and adjusts classifications based on responses, using techniques like render paths, memory moats, reification, fetch guards, and Penrose pathways to determine bot-like activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple challenges are provided to users over multiple instances, then bot detection accuracy is improved, but user experience complexity increases
Solution Approach 1:
The bot detection system segments the verification process into multiple independent challenges distributed across different user instances. Each challenge is a discrete test that can be passed or failed independently, allowing the system to build confidence in user authenticity gradually without overwhelming the user with a single complex verification process.
Solution Approach 2:
The system dynamically adjusts the number and type of challenges based on user behavior patterns and risk assessment. Users exhibiting suspicious behavior across multiple instances receive additional challenges, while legitimate users experience streamlined verification. This dynamic adaptation balances detection accuracy with user experience without applying fixed complexity to all users.
2Reliability
If user classification is adjusted based on challenge responses, then bot identification reliability is improved, but false positive risk increases
Solution Approach 1:
The system implements feedback loops where challenge responses are continuously analyzed and fed back into user classification. Each challenge result adjusts the user's risk score and classification level, allowing the system to learn from patterns across multiple instances. This iterative feedback mechanism improves reliability by considering cumulative evidence rather than isolated incidents.
Solution Approach 2:
The system changes classification parameters (such as scrutiny level, challenge frequency, and threshold values) based on user behavior patterns. Legitimate users experiencing temporary anomalies have their parameters adjusted temporarily, while bots showing consistent suspicious patterns have more stringent parameters applied. This parameter adaptation reduces false positives by contextualizing individual challenge failures within overall user behavior.
3Measurement precision
If multiple levels of scrutiny are applied to different user classifications, then bot detection thoroughness is improved, but processing time increases
Solution Approach 1:
The system applies different levels of scrutiny locally to different user classifications rather than uniformly to all users. High-risk users receive intensive multi-layered challenges with multiple scrutiny levels, while low-risk users experience minimal verification. This localized quality approach ensures thorough detection where needed while minimizing processing time for legitimate users.
Solution Approach 2:
The system applies partial scrutiny to most users and excessive scrutiny only to suspected bots. By using risk-based classification, the system performs basic verification on legitimate users (partial action) and reserves comprehensive multi-level challenges for high-risk cases (excessive action), optimizing the balance between detection thoroughness and processing efficiency.
Data Source
AI summary
Aspects and elements of the present disclosure relate to systems and methods for determining whether a user of an application is a bot, the sequences of computer-executable instructions including instructions that instruct at least one processor to assign the user to a user classification of a plurality of user classifications for the application, provide, responsive to assigning the user to the user classification, one or more challenges of a plurality of challenges to the user over multiple instances of the user using the application, each challenge of the plurality of challenges being configured to determine whether the user is a bot, and each challenge being associated with at least one user classification of the plurality of user classifications, and change the user classification based on the user's response to the one or more challenges.