Dynamic Risk-Aware Patch Scheduling Algorithms
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Solution Overview
Problem
Existing patch management systems rely on static vulnerability scoring systems like CVSS, which fail to capture the dynamic nature of exploitation risks over time and do not consider the time cost of applying patches, leading to suboptimal patch scheduling decisions, especially for organizations with limited resources.
Innovation Solution
The development of a dynamic risk metric that incorporates the probability of exploit over time, using machine learning to predict this probability, and employing two dynamic risk-aware patch scheduling algorithms: baseline scheduling for optimal risk reduction with high computation overhead, and group-based scheduling for lower computation cost with effective risk reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static vulnerability scoring systems (CVSS) are used to prioritize patches, then patch scheduling is simple and fast, but the system cannot capture the dynamic nature of exploitation risks over time
Solution Approach 1:
The patent transforms the static CVSS scoring system into a dynamic risk assessment model that updates vulnerability risks over time. The risk score evolves based on time-dependent factors including exploit availability probability, attacker interest, and system exposure duration, allowing the patch prioritization to adapt as new information becomes available about each vulnerability.
Solution Approach 2:
The system incorporates feedback loops where exploit availability data, attacker behavior patterns, and patch application outcomes continuously inform and update the risk assessment model. This feedback mechanism allows the system to learn from actual exploitation attempts and adjust patch prioritization dynamically, improving both accuracy and adaptability.
2Ease of operation
If patch prioritization is based solely on overall risk score, then scheduling is straightforward, but it ignores the time cost of applying patches leading to suboptimal decisions
Solution Approach 1:
The patent introduces multiple parameters beyond the traditional risk score, including time-to-patch, exploit availability probability, and cumulative risk exposure. These additional parameters transform the single-dimensional prioritization into a multi-parameter optimization problem that accounts for both risk magnitude and the temporal cost of remediation, enabling more reliable security decisions.
3Measurement precision
If dynamic risk assessment with machine learning is implemented, then exploitation probability is accurately predicted, but computation overhead increases significantly
Solution Approach 1:
The patent segments the vulnerability assessment process into distinct computational components: feature extraction from vulnerability data, time-dependent risk modeling, and exploit probability prediction. This segmentation allows each component to be optimized independently and enables parallel processing, reducing overall computation overhead while maintaining prediction accuracy.
4Reliability
If organizations patch all vulnerabilities immediately, then security risk is minimized, but resource constraints make this impossible for small and medium organizations
Solution Approach 1:
The patent implements partial action by prioritizing patching efforts for the most critical vulnerabilities based on dynamic risk assessment rather than attempting to patch all vulnerabilities simultaneously. The system identifies a subset of high-risk vulnerabilities that should be patched immediately while deferring lower-priority patches to later time periods when resources are available, achieving effective risk reduction with limited resources.
Data Source
AI summary
A program that defines and assesses the dynamic risk of software vulnerabilities and considers the dynamic risks into patch scheduling to reduce security risks posed by vulnerabilities and provide formal guidance to security operations at various organizations.