WS-ADVISING: a Reusable and reconfigurable microservices‐based platform for effective academic advising

Driss, Maha

doi:10.1007/s12652-021-02901-x

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Furthermore, we intend to look into the promising field of explainable FL [45], which refers to strategies for providing human-readable insights into data, variables, and decisions. Finally, future developments will use DL models as microservices-based architecture [46][47][48] to construct our suggested solution in a distributed architectural style. DL as a microservice is a self-contained, locally executable, and easily testable and maintainable software unit that can be reused and adjusted for a wide range of features and datasets by simply altering configuration parameters.…”

Section: Discussionmentioning

confidence: 99%

A federated learning framework for cyberattack detection in vehicular sensor networks

Driss

Almomani

Huma

et al. 2022

Complex Intell. Syst.

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Vehicular Sensor Networks (VSN) introduced a new paradigm for modern transportation systems by improving traffic management and comfort. However, the increasing adoption of smart sensing technologies with the Internet of Things (IoT) made VSN a high-value target for cybercriminals. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques attracted the research community to develop security solutions for IoT networks. Traditional ML and DL approaches that operate with data stored on a centralized server raise major privacy problems for user data. On the other hand, the resource-constrained nature of a smart sensing network demands lightweight security solutions. To address these issues, this article proposes a Federated Learning (FL)-based attack detection framework for VSN. The proposed scheme utilizes a group of Gated Recurrent Units (GRU) with a Random Forest (RF)-based ensembler unit. The effectiveness of the suggested framework is investigated through multiple performance metrics. Experimental findings indicate that the proposed FL approach successfully detected the cyberattacks in VSN with the highest accuracy of 99.52%. The other performance scores, precision, recall, and F1 are attained as 99.77%, 99.54%, and 99.65%, respectively.

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Section: Discussionmentioning

confidence: 99%

A federated learning framework for cyberattack detection in vehicular sensor networks

Driss

Almomani

Huma

et al. 2022

Complex Intell. Syst.

Self Cite

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“…Finally, future extensions will introduce DL as a microservice as an architectural style for designing our proposed approach. DL as a microservice is a standalone, locally runnable, and simply reconfigurable software unit that can be reused and customized for diverse features and datasets only by changing structured configuration parameters 52–54 …”

Section: Discussionmentioning

confidence: 99%

Fusion of convolutional neural networks based on Dempster–Shafer theory for automatic pneumonia detection from chest X‐ray images

Atitallah

Driss

Boulila

et al. 2021

Int J Imaging Syst Tech

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Deep learning‐based applications for disease detection are essential tools for experts to effectively diagnose diseases at different stages. In this article, a new approach based on an evidence based fusion theory is proposed, allowing the combination of a set of deep learning classifiers to provide more accurate disease detection results. The main contribution of this work is the application of the Dempster–Shafer theory for the fusion of five pre trained convolutional neural networks including VGG16, Xception, InceptionV3, ResNet50, and DenseNet201 for the diagnosis of pneumonia from chest X‐ray images. To evaluate this approach, experiments are conducted using a publicly available dataset containing more than 5800 chest X‐ray images. The obtained results demonstrate that our approach provides excellent detection performance compared to other state‐of‐the‐art methods; it achieves a precision of 97.5%, a recall of 98%, an f1‐score of 97.8%, and an accuracy of 97.3%.

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“…This paradigm provides the software developers with guidelines and reduces the implementation efforts by offering a lightweight, flexible, and scalable process for building and running applications. Furthermore, the microservices architecture offers multiple other benefits [19] architectures [20] [21] such as the enhancement of service maintainability, deployability, testability, scalability, composability, and resilience. Thus, it enables organizations to quickly and efficiently develop more adaptable and reusable software solutions.…”

Section: Microservices: Architecture and Essential Characteristicsmentioning

confidence: 99%

Microservices in IoT Security: Current Solutions, Research Challenges, and Future Directions

Driss

Hasan

Boulila

et al. 2021

Procedia Computer Science

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WS-ADVISING: a Reusable and reconfigurable microservices‐based platform for effective academic advising

Cited by 5 publications

References 15 publications

A federated learning framework for cyberattack detection in vehicular sensor networks

A federated learning framework for cyberattack detection in vehicular sensor networks

Fusion of convolutional neural networks based on Dempster–Shafer theory for automatic pneumonia detection from chest X‐ray images

Microservices in IoT Security: Current Solutions, Research Challenges, and Future Directions

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