Systematic Literature Review of Security Pattern Research

Washizaki, Hironori; Xia, Tian; Kamata, Natsumi; Fukazawa, Yoshiaki; Kanuka, Hideyuki; Kato, Tsuyoshi; Yoshino, Masayuki; Okubo, Takao; Ogata, Shinpei; Kaiya, Haruhiko; Hazeyama, Atsuo; Tanaka, Takafumi; Yoshioka, Nobukazu; Priyalakshmi, G.

doi:10.3390/info12010036

Cited by 11 publications

(2 citation statements)

References 199 publications

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“…Moreover, these applications are rather complex, and this makes it difficult to exploit them to "unit test" the implemented analyses. We instead decided to implement a mock microservice application that focused on security and that would adhere to well-known architectural security patterns [46,47]. The idea was to have a microservice application without any security smells and to artificially introduce them to test the analyses.…”

Section: Controlled Experimentsmentioning

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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Section: Controlled Experimentsmentioning

confidence: 99%

KubeHound: Detecting Microservices’ Security Smells in Kubernetes Deployments

Dell’Immagine,

Soldani,

Brogi

2023

Future Internet

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“…Conventionally, resources in cloud computing are located in a huge data storage center and are managed and controlled by a third party, who offers computing infrastructures for the cloud users to be used by anyone from anywhere via the Internet [3]. Cloud computing presents a huge resource that can be reached on demand and distributed over the Internet [4][5][6][7][8]. Figure 1 illustrates the cloud computing model environment for cloud services allow users (including persons, businesses, and customers) to exploit software and hardware that are managed at remote locations by third parties.…”

Section: Introduction 1cloud Computingmentioning

confidence: 99%

Fog Computing, Cloud Computing and IoT Environment: Advanced Broker Management System

Masarweh

Alwada'n

Afandi

2022

JSAN

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Cloud computing is a massive amount of dynamic ad distributed resources that are delivered on request to clients over the Internet. Typical centralized cloud computing models may have difficulty dealing with challenges caused by IoT applications, such as network failure, latency, and capacity constraints. One of the introduced methods to solve these challenges is fog computing which makes the cloud closer to IoT devices. A system for dynamic congestion management brokerage is presented in this paper. With this proposed system, the IoT quality of service (QoS) requirements as defined by the service-level agreement (SLA) can be met as the massive amount of cloud requests come from the fog broker layer. In addition, a forwarding policy is introduced which helps the cloud service broker to select and forward the high-priority requests to the appropriate cloud resources from fog brokers and cloud users. This proposed idea is influenced by the weighted fair queuing (WFQ) Cisco queuing mechanism to simplify the management and control of the congestion that may possibly take place at the cloud service broker side. The system proposed in this paper is evaluated using iFogSim and CloudSim tools, and the results demonstrate that it improves IoT (QoS) compliance, while also avoiding cloud SLA violations.

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Cloud Computing Infrastructure, Platforms, and Software for Scientific Research

Mathur

2024

Series in BioEngineering

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Systematic Literature Review of Security Pattern Research

Cited by 11 publications

References 199 publications

KubeHound: Detecting Microservices’ Security Smells in Kubernetes Deployments

KubeHound: Detecting Microservices’ Security Smells in Kubernetes Deployments

Fog Computing, Cloud Computing and IoT Environment: Advanced Broker Management System

Cloud Computing Infrastructure, Platforms, and Software for Scientific Research

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