Adaptive Service Request Scoring via Dynamic Score Offset
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Solution Overview
Problem
Heuristic analysis systems in computing services face vulnerabilities to probing mechanisms and false positives due to rigid score threshold comparisons, which can allow misuse while incorrectly penalizing legitimate users.
Innovation Solution
Implementing a score offset mechanism that adjusts scores based on past user activity patterns and decays over time, allowing for more flexible evaluation of user requests and reducing the incidence of false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a rigid score threshold comparison is used to evaluate user requests, then the system can clearly distinguish between compliant and non-compliant requests, but the system becomes vulnerable to probing mechanisms and produces false positives
Solution Approach 1:
The patent applies dynamics by making the score threshold adaptive rather than static. The system dynamically adjusts thresholds based on user behavior patterns, request contexts, and historical data. This allows the threshold to flexibly accommodate legitimate variations in user behavior while maintaining sensitivity to actual policy violations, thereby reducing false positives while preserving measurement precision.
Solution Approach 2:
The system changes the parameter of score threshold from a fixed value to a variable that can be adjusted based on multiple factors including user reputation, request patterns, and contextual information. This parameter change enables the system to differentiate between benign requests that temporarily exceed static thresholds and genuine policy violations, reducing probing effectiveness while maintaining accurate compliance detection.
2Ease of operation
If a fixed score threshold is applied to all requests, then the enforcement is consistent and simple, but legitimate user activity may be incorrectly identified as misuse
Solution Approach 1:
The patent applies local quality by customizing score thresholds for different users, contexts, and request types rather than using a universal fixed threshold. The system evaluates local characteristics such as user history, request patterns, and contextual factors to determine appropriate thresholds for each specific case. This localized approach prevents legitimate users from being incorrectly penalized while maintaining enforcement consistency through systematic local evaluations.
Solution Approach 2:
The system performs preliminary actions by pre-establishing user profiles, reputation scores, and baseline behavior patterns before evaluating requests. This preliminary characterization allows the system to contextualize score threshold evaluations, understanding whether a request exceeds a threshold due to legitimate reasons or actual misuse, thereby preventing false penalties while maintaining operational simplicity through pre-computed contextual data.
3Reliability
If the system denies requests that exceed the score threshold, then policy compliance is enforced, but probing users can incrementally adapt requests to find variations that satisfy the threshold
Solution Approach 1:
The patent implements feedback by using the outcomes of score evaluations and request denials to continuously learn and adapt to probing behaviors. The system analyzes patterns in denied requests, identifies adaptive strategies employed by probing users, and adjusts score thresholds and evaluation criteria accordingly. This feedback loop maintains reliable policy enforcement while progressively closing evasion gaps that probing users exploit.
Solution Approach 2:
The system applies preliminary anti-action by proactively anticipating probing attempts and pre-adjusting score thresholds based on detected probing patterns. When probing behavior is identified, the system preemptively modifies evaluation criteria and thresholds to counteract the probing strategy before it can successfully evade enforcement. This preliminary counter-measure maintains policy compliance while reducing the adaptability of probing users.
Data Source
AI summary
Services often utilize scoring techniques to distinguish between user requests that comply with a usage policy of the service from those that represent a misuse of the service. Users who endeavor to misuse the service engages in probing by submitting a variety of requests to the service until one such request exhibiting a score that is within a score threshold, and then patterning further requests that misuse the service upon the successful request. Instead, when a first request from a user is identified that violates the score threshold, a score offset is selected. The scores of second and future requests by the user are adjusted by the score offset, indicating increased suspicion of the user's requests, while allowing legitimate requests that fulfill the score threshold by a larger margin. Additionally, absent further misuse, the score offset decays over time to restore trust incrementally in the legitimacy of the user's requests.