IoT and Machine Learning-Based Hypoglycemia Detection System

Sifat, Fahim Hossain; Mahzabin, Rahnuma; Anjum, Sadia; Nayan, Al-Akhir; Kibria, Muhammad Golam

doi:10.1109/iciset54810.2022.9775890

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…In the article (Sifat et al, 2022), the authors have access to a review of the typology of anomalies, the detection layers, the context and the methodology. What emerges is an oversimplified view of anomaly classification.…”

Section: Literature Review Of Existing Modelsmentioning

confidence: 99%

Cybersecurity Mechanism for Automatic Detection of IoT Intrusions Using Machine Learning

Seyed,

Kebe,

El Arby

et al. 2024

Journal of Computer Science

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This article proposes an ML-based cyber security mechanism to optimize intrusion detection that attacks internet objects (IoT). Our approach consists of bringing together several learning methods namely supervised learning, unsupervised learning and reinforcement learning within the same Canvas. The objective is to choose among them the most optimal for classifying and predicting attacks while minimizing the impact linked to the learning costs of these attacks. In our proposed model, we have used a modular design to facilitate the implementation of the intrusion detection engine. The first Meta-learning module is used to collect metadata related to existing algorithmic parameters and learning methods in ML. As for the second module, it allows the use of a cost-sensitive learning technique so that the model is informed of the cost of intrusion detection scenarios. Therefore, among the ML classification algorithms, we choose the one whose automatic learning of intrusions is the least expensive in terms of its speed and its quality in predicting reality. This will make it possible to control the level of acceptable risk in relation to the typology of cyber-attacks. We then simulated our solution using the Weka tool. This led to questionable results, which can be subject to the evaluation of model performance. These results show that the classification quality rate is 93.66% and the classification consistency rate is 0.882 (close to unit 1). This proves the accuracy and performance of the model.

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Section: Literature Review Of Existing Modelsmentioning

confidence: 99%

Cybersecurity Mechanism for Automatic Detection of IoT Intrusions Using Machine Learning

Seyed,

Kebe,

El Arby

et al. 2024

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…In the article [11], the authors have access to a review of the typology of anomalies, detection layers, context and methodology. What emerges is an overly simplistic view of anomaly classification.…”

Section: Related Workmentioning

confidence: 99%

Proposal of a Machine Learning-based Model to Optimize the Detection of Cyber-attacks in the Internet of Things

Seyed,

NGO,

KEBE

2023

IJACSA

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In this article, we propose a model to optimize the detection of attacks in IoT. IoT network is a promising technology that connects living and non-living things around the world. Despite the increased development of these technologies, cyber-attacks remains a weakness, making it vulnerable to numerous cyber-attacks. Of course, automatic computer intrusion detection systems are deployed. However, it does not make it possible to mobilize the full potential of Machine Learning. Our approach in this maneuver consists of offering a means to select the least expensive ML method in terms of learning in order to optimize the prediction of threats to introduce IoT objects. To do this, we make modular design based on two layers. The first module is a canvas containing the different methods most used in ML such as supervised learning method, unsupervised learning method and reinforcement learning method. The second module introduces a mechanism to measure the learning cost linked to each of these methods in order to choose the least expensive one in order to quickly and efficiently detect intrusions in IoT objects. To prove the validity of the proposed model, we simulated it using the Weka tool. The results obtained illustrate the following behaviors: The classification quality rate is 93.66%. This last result is supported by a classification consistency rate of 0.882 (close to unity 1) demonstrating a trend towards convergence between observation and prediction.

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“…The cultivation of grapes, known as viticulture, is a rapidly expanding agricultural sector in Mediterranean countries. The situation in Portugal is illustrative because the country's vineyards cover an estimated total area of around 191,000 hectares, making it the fourth-largest in Europe [1]. Increased demand for Mediterranean wines abroad is having a significant economic impact on the winemaking industry there.In order to prevent the wild species, sometimes known as weeds, from competing with the vines for sunlight and nutrients, they must be routinely removed in the spring.…”

Section: Introductionmentioning

confidence: 99%

Technology based on the Internet of Things to Monitor Animals

Boga¹,

T.Sunitha²,

Manjula³

2023

IJSMEM

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Improving existing animal husbandry practices is essential before introducing grazing animals to vineyards. In order to provide this type of assistance, it is necessary to monitor and condition the animals’ whereabouts and actions, especially their feeding posture. Using this strategy, sheep could graze in agricultural areas (such vineyards and orchards) without fear of harming them. Based on these findings, we have created an IoT-based platform for tracking animal habits. To facilitate unattended shepherding of ovine within vineyard areas, the system integrates a local Internet of Things network for data collection from the animals with a cloud platform with data dispensationalso storage competences. As a result, the system can tend to ovine flocks. Easy analysis and interpretation of Internet of Things (IoT) data is made possible by the machine learning capabilities built into the cloud platform. Therefore, we shall not only outline the platform but also supply some machine learning platform-specific results. To be more specific, testing looked at how well this platform could identify and characterize disorders related to animal posture. This page offers a comparison of the tested approaches because multiple algorithms were used.

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IoT and Machine Learning-Based Hypoglycemia Detection System

Cited by 5 publications

References 25 publications

Cybersecurity Mechanism for Automatic Detection of IoT Intrusions Using Machine Learning

Cybersecurity Mechanism for Automatic Detection of IoT Intrusions Using Machine Learning

Proposal of a Machine Learning-based Model to Optimize the Detection of Cyber-attacks in the Internet of Things

Technology based on the Internet of Things to Monitor Animals

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