Reputation-Based Security Vulnerability Detection

The patent implements preliminary action by establishing a reputation system that assigns trust scores to applications before they can exploit vulnerabilities. The system proactively monitors application behavior, system calls, and resource access patterns in advance, maintaining a reputation database that evaluates applications before they can cause harm. This allows the system to detect and respond to potential threats before they become actual security incidents, rather than waiting for retroactive analysis after damage occurs.

The patent implements feedback by continuously monitoring application behavior and updating reputation scores based on observed actions. The system collects data from multiple sources including system calls, resource access patterns, and behavioral anomalies, then feeds this information back into the reputation evaluation mechanism. This closed-loop feedback system dynamically adjusts trust levels and can automatically respond to changing threat conditions, enabling adaptive security that improves over time rather than relying on static detection rules.

The patent implements local quality by applying different levels of monitoring intensity and reputation evaluation granularity to different applications based on their risk profiles. High-risk applications with lower reputation scores receive more intensive monitoring and resource allocation, while trusted applications with high reputation scores receive minimal monitoring. This localized approach concentrates computational resources where they are most needed for security detection, rather than uniformly monitoring all applications at the same level.

The patent implements parameter changes by dynamically adjusting monitoring thresholds, sampling rates, and analysis depth based on application reputation scores and current threat levels. When an application's reputation deteriorates or suspicious behavior is detected, the system automatically increases monitoring intensity and resource allocation. Conversely, trusted applications receive reduced monitoring overhead. This dynamic parameter adjustment allows the system to maintain high security detection capability while optimizing resource consumption based on actual risk conditions.

The patent implements partial action by selectively monitoring only the most relevant system calls, behavior patterns, and resource access events based on application reputation and risk profiles. Rather than analyzing every single system event in detail, the system focuses computational resources on suspicious or high-risk behaviors while using lighter-weight monitoring for trusted applications. This selective partial monitoring maintains high detection accuracy for critical threats while reducing overall analysis time and computational overhead.

The patent implements periodic action by conducting reputation evaluations and detailed behavior analysis at scheduled intervals rather than continuously in real-time. The system uses periodic sampling of application behavior combined with event-triggered analysis when suspicious activities occur. This periodic monitoring approach, supplemented by on-demand deep analysis when needed, maintains measurement precision for detecting vulnerabilities while significantly reducing the average time required for continuous analysis compared to exhaustive real-time monitoring of all behaviors.