Table I from CHeSS: A Configuration Health Scoring System and Its Application to Network Devices | Semantic Scholar (2024)

This paper shows how CHI, which is defined as a configuration scoring system, can take advantage of the domain knowledge and the available available performance data to produce important insights into the configuration settings, and compares the CHI with both well-advertised segmented non-linear models and state-of-the-art data-driven models.

This paper presents a generic data model for configuration information of network devices that takes into account vendor and version heterogeneity, and proposes a configuration query engine that will allow a user to pinpoint a specific, abstract configuration parameter, and be given the proper sequence of commands to query that parameter on a device of given vendor and operating system version.

The Common Vulnerability Scoring System is a public initiative designed to address this issue by presenting a framework for assessing and quantifying the impact of software vulnerabilities.

A holistic and structured overview of the systems approaches that tackle configuration errors and the potential solutions for resolving configuration errors in the spectrum of system development and management is provided.

The key idea is to use statistical data mining techniques on large-scale active monitoring data to determine a ranked list of suspect causes, which is refined with passive monitoring signals and correlated with other failure signals from passive monitoring to increase the confidence of the probabilistic analysis.

A suite of complexity models are developed that describe the routing design and configuration of a network in a succinct fashion, abstracting away details of the underlying configuration languages and are predictive of the issues operators face when reconfiguring their networks.

A semantic-based approach is presented that eases and automates the configuration of network devices while complementing the capabilities of OF-CONFIG and NETCONF and defines a learning algorithm that enables automated interpretation of CLIs' configuration capabilities in heterogeneous (multi-vendor) network scenarios.

This work shows how to train classifiers that predict much more accurately whether and how soon individual vulnerabilities are likely to be exploited, compared to current industry-standard heuristics based on expert knowledge and static formulas.

One of the first attempts to conduct a real-world misconfiguration characteristic study is undertaken, finding that a significant portion of misconfigurations can cause hard-to-diagnose failures, such as crashes, hangs, or severe performance degradation, indicating that systems should be better-equipped to handle mis configurations.

There was no single root cause: it often changed with the workload, hardware, or software configuration in the storage stack, and the amount of variability in benchmarking modern storage stacks was characterized.

The architecture of WSCs is described, the main factors influencing their design, operation, and cost structure, and the characteristics of their software base are described.

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