Network Device Vulnerability Prediction via Survival Analysis

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Solution Overview

Problem

Network device vulnerabilities often go undetected until they cause service degradation, leading to unplanned outages and decreased customer satisfaction, as existing systems rely on reactive rather than predictive maintenance.

Innovation Solution

A system that includes a processor configured to detect network device outages, determine their validity, and predict vulnerabilities using survival analysis, with a graphical user interface to display predicted vulnerabilities, enabling proactive maintenance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If reactive maintenance is used, then current system simplicity is maintained, but vulnerability detection timing is delayed until service degradation occurs

Engineering Contradiction:

Improvevulnerability detection timingVSAvoidtime to detect vulnerability

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by continuously monitoring network device metrics and using survival analysis to predict vulnerabilities before they manifest as actual failures. The survival analysis model processes historical failure data and current device state to forecast time-to-failure, enabling proactive maintenance scheduling before service degradation occurs.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If predictive maintenance using survival analysis is implemented, then vulnerability detection timing is improved, but system complexity increases

Engineering Contradiction:

Improvevulnerability detection timingVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system introduces survival analysis as an intermediary layer between raw network device data and maintenance decisions. This intermediary model processes complex monitoring data through statistical algorithms, transforming raw metrics into predictive vulnerability assessments. The survival analysis acts as a mediator that simplifies the complexity of predicting device failures by using established statistical methods.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If continuous monitoring and survival analysis are performed, then preventive maintenance capability is improved, but computational resource consumption increases

Engineering Contradiction:

Improvepreventive maintenance capabilityVSAvoidcomputational resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by selectively monitoring and analyzing only the most critical network devices and metrics rather than continuously processing all device data. The survival analysis model focuses computational resources on devices with highest risk profiles, using risk prioritization to determine which devices require intensive monitoring and which can be monitored more lightly.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11012461B2Network device vulnerability prediction

Publication Date: 2021.05.18 ACCENTURE GLOBAL SOLUTIONS LTD

AI summary

The vulnerability of network devices may be predicted by performing a survival analysis on big data. A prediction algorithm may be built by considering historical data from heterogeneous data sources. The operating state of the network devices on a network may be predicted. The services potentially affected by a predicted outage may be determined and displayed. Alternatively or in addition, the number of clients potentially affected by a predicted outage may be determined and displayed.