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

Driss, Maha; Hasan, Daniah; Boulila, ‪Wadii; Ahmad, Jawad

doi:10.1016/j.procs.2021.09.007

Cited by 48 publications

(19 citation statements)

References 28 publications

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“…In today’s IoMT environment, cloud-based technologies are widely used [ 122 ]. This layer collects and filters data from perception layer equipment (sensors), discovers services, and controls device access [ 123 ]. A Hewlett–Packard (HP) investigation [ 121 ] claims that session hijacking and cross-site request forgery (CSRF) breaches have been discovered in numerous IoT projects, affecting 60% of all IoT devices connected to the cloud.…”

Section: Iomt Security and Privacymentioning

confidence: 99%

A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

Shakeel

Habib

Boulila

et al. 2022

Complex Intell. Syst.

Self Cite

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Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) has demonstrated the ability to connect various medical apparatus, sensors, and healthcare specialists to ensure the best medical treatment in a distant location. Patient safety has improved, healthcare prices have decreased dramatically, healthcare services have become more approachable, and the operational efficiency of the healthcare industry has increased. This research paper offers a recent review of current and future healthcare applications, security, market trends, and IoMT-based technology implementation. This research paper analyses the advancement of IoMT implementation in addressing various healthcare concerns from the perspectives of enabling technologies, healthcare applications, and services. The potential obstacles and issues of the IoMT system are also discussed. Finally, the survey includes a comprehensive overview of different disciplines of IoMT to empower future researchers who are eager to work on and make advances in the field to obtain a better understanding of the domain.

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Section: Iomt Security and Privacymentioning

confidence: 99%

A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

Shakeel

Habib

Boulila

et al. 2022

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

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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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“…The demand for water increases, and the need to develop an intelligent system becomes indispensable [1]. An alternative that has become increasingly popular, with multiple benefits in many areas, is based on the concept of the Internet of Things (IoT) [2]. IoT has made significant contributions in various fields, including being integrated into intelligent water management systems [3].…”

Section: Introductionmentioning

confidence: 99%

Advanced Strategies for Monitoring Water Consumption Patterns in Households Based on IoT and Machine Learning

Arsene

Predescu

Pahonțu

et al. 2022

Water

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Water resource management represents a fundamental aspect of a modern society. Urban areas present multiple challenges requiring complex solutions, which include multidomain approaches related to the integration of advanced technologies. Water consumption monitoring applications play a significant role in increasing awareness, while machine learning has been proven for the design of intelligent solutions in this field. This paper presents an approach for monitoring and predicting water consumption from the most important water outlets in a household based on a proposed IoT solution. Data processing pipelines were defined, including K-means clustering and evaluation metrics, extracting consumption events, and training classification methods for predicting consumption sources. Continuous water consumption monitoring offers multiple benefits toward improving decision support by combining modern processing techniques, algorithms, and methods.

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

Cited by 48 publications

References 28 publications

A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

A federated learning framework for cyberattack detection in vehicular sensor networks

Advanced Strategies for Monitoring Water Consumption Patterns in Households Based on IoT and Machine Learning

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