Automatic extraction of security-rich dataflow diagrams for microservice applications written in Java

Schneider, Simon; Scandariato, Riccardo

doi:10.1016/j.jss.2023.111722

Cited by 11 publications

(1 citation statement)

References 55 publications

(74 reference statements)

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“…For instance, Chondamrongkul et al [9] propose a technique to automatically identify security threats in microservice applications, starting from a collection of formally defined security characteristics. Other examples are given by Schneider et al [10] and Zdun et al [11], who support checking microservice applications for security by extracting security-aware dataflow diagrams and providing KPI metrics to measure the effectiveness in securing applications, respectively. Automatically detecting the security smells proposed by Ponce et al [1] in microservice applications is, however, still an open problem [8], as also witnessed by the recent review by Pinheiro et al [12].…”

Section: Related Workmentioning

confidence: 99%

KubeHound: Detecting Microservices’ Security Smells in Kubernetes Deployments

Dell’Immagine,

Soldani,

Brogi

2023

Future Internet

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As microservice-based architectures are increasingly adopted, microservices security has become a crucial aspect to consider for IT businesses. Starting from a set of “security smells” for microservice applications that were recently proposed in the literature, we enable the automatic detection of such smells in microservice applications deployed with Kubernetes. We first introduce possible analysis techniques to automatically detect security smells in Kubernetes-deployed microservices. We then demonstrate the practical applicability of the proposed techniques by introducing KubeHound, an extensible prototype tool for automatically detecting security smells in microservice applications, and which already features a selected subset of the discussed analyses. We finally show that KubeHound can effectively detect instances of security smells in microservice applications by means of controlled experiments and by applying it to existing, third-party applications.

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