Internet of Things Security: A Survey

Sadeeq, Mohammed A. M.; Zeebaree, Subhi R. M.; Qashi, Riyadh; Ahmed, Sarkar Hasan; Jacksi, Karwan

doi:10.1007/978-981-33-6835-4_7

Cited by 25 publications

(2 citation statements)

References 89 publications

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“…Be that as it may, these days, there are numerous executions of the web, which is noticed anyplace in a few types of life as a bunch of gave applications and administrations by meeting clients' prerequisites paying little heed to the area and the time. This is an immediate result of the client and components digitalization [1].…”

Section: Introductionmentioning

confidence: 99%

Smart Identification System on Internet of Things for User Authentication

Antonidoss¹,

Bhavani²,

Prabha³

et al. 2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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Regarding that, clients agreed on the value of reliable Internet access regardless of location, mode, or time. Individuals can now access a few top-tier administrations through the Internet of Things (IoT), which is a heterogeneous network characterized by machine-to-machine communication. Regardless of how the devices are used to set up the communications, the clients should be considered the true producers of data and purchasers of the yield data. As a result, clients should be viewed as a critical component of IoT; as a result, client distinguishing evidence, verification, and approval are required. In any case, the client Index measure is too perplexing, given the clients' apprehension about sharing their confidential and private information. However, some of their devices should be able to access this personal information. Appropriately, an unbiased method for understanding clients and coping with their personalities is important. Furthermore, the customer plays a minor but significant role in laying the groundwork for the character distinguishing proof and ensuring the consistency of open services. The main goal of this paper is to create a new identity management system (IdMS) for the Internet of Things. The main commitments of this paper are the suggestion of a gadget acknowledgment calculation for client identifiable evidence, the suggestion of a different identifier configuration, and a hypothetical IdMS framework.

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Section: Introductionmentioning

confidence: 99%

Smart Identification System on Internet of Things for User Authentication

Antonidoss¹,

Bhavani²,

Prabha³

et al. 2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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show abstract

“…In April 2013 Arai and Andrie described a method for extract features from the skeleton human body using support vector machine (SVM) that estimates average foot angle, where reached classification accuracy is about 85.33% [5]. In 2020, suggested analysis in the time domain of many features using classifier [17], [18].…”

mentioning

confidence: 99%

Using skeleton model to recognize human gait gender

Alsaif

Hasan

Maray

2023

IJ-AI

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Biometrics became fairly important to help people identifications persons by their individualities or features. In this paper, gait recognition has been based on a skeleton model as an important indicator in prevalent activities. Using the reliable dataset for the Chinese Academy of Sciences (CASIA) of silhouettes class C database. Each video has been discredited to 75 frames for each (20 persons (10 males and 10 females)) as (1.0), the result will be 1,500 frames. After Pre-processing the images, many features are extracted from human silhouette images. For gender classification, the human walking skeleton used in this study. The model proposed is based on morphological processes on the silhouette images. The common angle has been computed for the two legs. Later, principal components analysis (PCA) was applied to reduce data using feature selection technology to get the most useful information in gait analysis. Applying two classifiers artificial neural network (ANN) and Gaussian Bayes to distinguish male or female for each classifier. The experimental results for the suggested method provided significant accomplishing about (95.5%), and accuracy of (75%). Gender classification using ANN is more efficient from the Gaussian Bayes technique by (20%), where ANN technique has given a superior performance in recognition.

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Secure key agreement for multi-device home IoT environment

Garg

Lee

2020

Internet of Things

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Internet of Things Security: A Survey

Cited by 25 publications

References 89 publications

Smart Identification System on Internet of Things for User Authentication

Smart Identification System on Internet of Things for User Authentication

Using skeleton model to recognize human gait gender

Secure key agreement for multi-device home IoT environment

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