Dynamic Server Patch Grouping via Stream Clustering
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Solution Overview
Problem
In cloud computing environments, existing technologies face challenges in dynamically managing and patching server groups to mitigate vulnerabilities effectively, as machines often share common risks and vulnerabilities, leading to increased security threats and inefficiencies in patching processes.
Innovation Solution
A system and method that facilitates dynamic re-composition of patch groups using stream clustering, where a control device identifies and groups machines with common vulnerabilities, applies patches, and uses monitoring and learning components to predict and mitigate risks, enabling real-time adjustments and proactive security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machines are patched individually based on their specific vulnerabilities, then each machine receives targeted protection, but the patching process becomes inefficient and time-consuming
Solution Approach 1:
The patent groups multiple machines with common vulnerabilities into patch groups, allowing patches to be applied collectively to multiple machines simultaneously. This merging approach maintains security protection for each machine while significantly improving patching efficiency by reducing redundant operations.
Solution Approach 2:
The system creates universal patch groups that can be applied across multiple machines sharing common vulnerabilities. A single patch group serves multiple machines, making the patching process more efficient while maintaining appropriate security coverage for each machine type.
2Adaptability or versatility
If patch groups are statically defined, then the patching process is simple to manage, but the system cannot adapt to dynamically changing security threats and vulnerability patterns
Solution Approach 1:
The system dynamically creates and updates patch groups based on real-time vulnerability data and security threats. Patch groups are not static but are continuously recomposed as new vulnerabilities are discovered or existing ones are resolved, allowing the system to adapt to changing security landscapes while managing complexity through automated processes.
Solution Approach 2:
The system monitors vulnerability patterns and security threats, using this feedback to automatically adjust and recompose patch groups. This feedback mechanism ensures the system remains adaptable to new threats while the automation reduces the perceived complexity for users.
3Productivity
If all machines in a group are patched together, then patching efficiency is improved, but machines with different vulnerability profiles may receive unnecessary patches
Solution Approach 1:
The system applies the principle of local quality by tailoring patch groups to match specific vulnerability profiles of machine subsets. Rather than applying universal patches to all machines, the system creates targeted patch groups that contain only the patches relevant to each group's specific vulnerability characteristics, eliminating unnecessary patches while maintaining efficiency.
Data Source
AI summary
Techniques for dynamic server groups that can be patched together using stream clustering algorithms, and learning components in order to reuse the repeatable patterns using machine learning are provided herein. In one example, in response to a first risk associated with a first server device, a risk assessment component patches a server group to mitigate a vulnerability of the first server device and a second server device, wherein the server group is comprised of the first server device and the second server device. Additionally, a monitoring component monitors data associated with a second risk to the server group to mitigate the second risk to the server group.