Vulnerability Prediction System for Cloud Infrastructure
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Solution Overview
Problem
There are delays in detecting and addressing cybersecurity vulnerabilities in cloud-based IT infrastructure due to the time it takes for third-party scanners to integrate and publish updates, leading to extended exposure and increased risk of unauthorized access.
Innovation Solution
Implementing a system that predicts the existence of cybersecurity vulnerabilities before scanning by comparing configuration item information with vulnerability data, using machine-learning models to determine similarity, and generating predicted vulnerable items to alert and restrict access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If third-party scanners are used to detect vulnerabilities, then detection accuracy is improved, but detection time is increased
Solution Approach 1:
The system performs preliminary vulnerability prediction by comparing configuration item information against known vulnerability patterns before actual scanning occurs. This preliminary action identifies potentially vulnerable configuration items, allowing the scanning process to focus only on these high-risk targets rather than scanning all configuration items, thus reducing overall detection time while maintaining accuracy.
Solution Approach 2:
The vulnerability detection process is segmented into two distinct phases: (1) a prediction phase that quickly identifies potentially vulnerable configuration items using pattern matching, and (2) a verification phase that performs detailed scanning only on predicted vulnerable items. This segmentation separates the bulk filtering operation from the detailed analysis, reducing the time penalty associated with comprehensive scanning.
2Reliability
If comprehensive scanning is performed on all configuration items, then vulnerability detection coverage is improved, but system productivity is decreased
Solution Approach 1:
Instead of performing exhaustive scanning on all configuration items, the system applies partial action by conducting comprehensive analysis only on configuration items predicted to be vulnerable. The prediction mechanism identifies a subset of high-risk items, and scanning resources are concentrated on this subset, achieving sufficient detection coverage for critical vulnerabilities while improving overall system productivity.
3Loss of time
If vulnerability scanning is performed frequently, then detection timeliness is improved, but computational resources are increased
Solution Approach 1:
The system performs preliminary prediction of vulnerable configuration items before actual scanning, using lightweight pattern matching against known vulnerability signatures. This preliminary action creates a filtered list of high-risk items that requires detailed scanning, allowing the system to maintain frequent detection cycles with reduced computational overhead since most configuration items are quickly ruled out by the prediction stage.
4Loss of time
If real-time vulnerability detection is implemented, then security response time is improved, but system complexity is increased
Solution Approach 1:
The detection system is segmented into modular components: a prediction module that performs preliminary filtering using vulnerability patterns, and a scanning module that conducts detailed analysis on predicted vulnerable items. This modular segmentation allows the system to achieve real-time response capability while managing complexity through clear separation of concerns and independent, reusable components.
Data Source
AI summary
Systems and methods are disclosed that predict whether a configuration item of a service provider cloud infrastructure client instance has a vulnerability, prior to scanning for the client instance for the vulnerability. In particular, operating system and/or application information of the vulnerability may be compared to that of the configuration item, operating system and/or application information of past vulnerabilities may be compared to that of the vulnerability, additional vulnerabilities that are solved by solutions that remedy the vulnerability may be compared to the configuration, and/or a machine-learning model may be trained to determine how similar past vulnerabilities of the configuration item are to the vulnerability. Based on one or more of these comparisons, a predicted vulnerable item may be generated that indicates that the configuration item is subject to the vulnerability.