Data Usage and Access Control in Industrial Data Spaces: Implementation Using FIWARE

Muñoz-Arcentales, Andres; López-Pernas, Sonsoles; García-Pozo, Alejandro; Alonso, Andoni; Salvachúa, Joaquín; Huecas, Gabriel

doi:10.3390/su12093885

Cited by 20 publications

(30 citation statements)

References 39 publications

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“…As such, it considers the complete data lifecycle, from data acquisition through sensors and other IoT devices, to data processing using Big Data technologies and presentation to the end user. Our implementation relies on FIWARE GEs and commonly used open source technologies, a combination that has proven useful in the past for building other types of smart solutions such as digital twins [46], data usage controlled sharing environments [47,48], and enhanced authentication systems [49]. In this article, we show how, by combining these building blocks, we provide a robust, flexible, scalable, and secure way to provide context-aware data analytics in smart environments.…”

Section: Discussionmentioning

confidence: 99%

Enabling Context-Aware Data Analytics in Smart Environments: An Open Source Reference Implementation

Muñoz-Arcentales

López-Pernas

Conde

et al. 2021

Sensors

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In recent years, many proposals of context-aware systems applied to IoT-based smart environments have been presented in the literature. Most previous works provide a generic high-level structure of how a context-aware system can be operationalized, but do not offer clues on how to implement it. On the other hand, there are many implementations of context-aware systems applied to specific IoT-based smart environments that are context-specific: it is not clear how they can be extended to other use cases. In this article, we aim to provide an open-source reference implementation for providing context-aware data analytics capabilities to IoT-based smart environments. We rely on the building blocks of the FIWARE ecosystem and the NGSI data standard, providing an agnostic end-to-end solution that considers the complete data lifecycle, covering from data acquisition and modeling, to data reasoning and dissemination. In other words, our reference implementation can be readily operationalized in any IoT-based smart environment regardless of its field of application, providing a context-aware solution that is not context-specific. Furthermore, we provide two example use cases that showcase how our reference implementation can be used in a variety of fields.

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Section: Discussionmentioning

confidence: 99%

Enabling Context-Aware Data Analytics in Smart Environments: An Open Source Reference Implementation

Muñoz-Arcentales

López-Pernas

Conde

et al. 2021

Sensors

Self Cite

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“…Moreover, several studies [5,28,38,45,67] highlight the need for policies that can specify rules with respect to contextual information, such as GPS information in the context of mobile and ubiquitous applications. [16,75,101]. Usage control can be applied in different application contexts (e.g., digital rights management (DRM) and data privacy) or different fields (e.g., ICT and IoT).…”

Section: Specificationmentioning

confidence: 99%

Usage Control Specification, Enforcement, and Robustness: A Survey

Akaichi¹,

Kirrane²

2022

Preprint

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The management of data and digital assets poses various challenges, including the need to adhere to legal requirements with respect to personal data protection and copyright. Usage control technologies could be used by software platform providers to manage data and digital assets responsibly and to provide more control to data and digital asset owners. In order to better understand the potential of various usage control proposals, we collate and categorize usage control requirements, compare the predominant usage control frameworks based on said requirements, and identify existing challenges and opportunities that could be used to guide future research directions.CCS Concepts: • Security and privacy → Information accountability and usage control; • Social and professional topics → Computing / technology policy; Intellectual property; Privacy policies.

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“…mx y = wtI y − wt left(y) I left(y)− wt right(y) I right(y) (1) where in Equation ( 1) the mx y is importance of node and I is Impurity and wt is weight of left and right with number of samples Figure 2 shows the Random Forest classifier diagram of how it works. Several tresses are used for the predictions, and the voting approach is used for the target output.…”

Section: Random Forestmentioning

confidence: 99%

“…The data stored on the internet is growing day by day particularly for threats that target sensitive or crucial data, and this has raised many security issues, such as malicious intrusions [1]. Targeted attacks and threats such as malware and botnets cause great damage to the community in different factors, such as financial loss or health loss.…”

Section: Introductionmentioning

confidence: 99%

Real-Time DDoS Attack Detection System Using Big Data Approach

et al. 2021

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Currently, the Distributed Denial of Service (DDoS) attack has become rampant, and shows up in various shapes and patterns, therefore it is not easy to detect and solve with previous solutions. Classification algorithms have been used in many studies and have aimed to detect and solve the DDoS attack. DDoS attacks are performed easily by using the weaknesses of networks and by generating requests for services for software. Real-time detection of DDoS attacks is difficult to detect and mitigate, but this solution holds significant value as these attacks can cause big issues. This paper addresses the prediction of application layer DDoS attacks in real-time with different machine learning models. We applied the two machine learning approaches Random Forest (RF) and Multi-Layer Perceptron (MLP) through the Scikit ML library and big data framework Spark ML library for the detection of Denial of Service (DoS) attacks. In addition to the detection of DoS attacks, we optimized the performance of the models by minimizing the prediction time as compared with other existing approaches using big data framework (Spark ML). We achieved a mean accuracy of 99.5% of the models both with and without big data approaches. However, in training and testing time, the big data approach outperforms the non-big data approach due to that the Spark computations in memory are in a distributed manner. The minimum average training and testing time in minutes was 14.08 and 0.04, respectively. Using a big data tool (Apache Spark), the maximum intermediate training and testing time in minutes was 34.11 and 0.46, respectively, using a non-big data approach. We also achieved these results using the big data approach. We can detect an attack in real-time in few milliseconds.

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Data Usage and Access Control in Industrial Data Spaces: Implementation Using FIWARE

Cited by 20 publications

References 39 publications

Enabling Context-Aware Data Analytics in Smart Environments: An Open Source Reference Implementation

Enabling Context-Aware Data Analytics in Smart Environments: An Open Source Reference Implementation

Usage Control Specification, Enforcement, and Robustness: A Survey

Real-Time DDoS Attack Detection System Using Big Data Approach

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