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IoT, Volume 3, Issue 2

June 2022 - 4 articles

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Cover Story: Detecting abnormal traffic is one of the problematic areas for researchers in protecting network infrastructures from adversary activities. Numerous automatic approaches can detect abnormal traffic. However, accuracy is not the only issue with current intrusion detection systems, as their efficiency, flexibility, and scalability need to be enhanced to detect attack traffic from various IoT networks. Thus, this study concentrates on constructing an ensemble classifier using the proposed integrated evaluation metrics (IEMs) to determine the best performance of IDS models. The automated ranking and best selection method (RBSM) is performed using the proposed IEMs to select the best model for the ensemble classifier to detect highly accurate attacks using machine and deep learning approaches. View this paper

Articles (4)

  • Article
  • Open Access
7 Citations
5,278 Views
17 Pages
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Expert Demand for Consumer Sleep Technology Features and Wearable Devices: A Case Study

  • Jaime K Devine,
  • Lindsay P. Schwartz,
  • Jake Choynowski and
  • Steven R Hursh
IoT2022, 3(2), 315-331;https://doi.org/10.3390/iot3020018

8 June 2022

Global demand for sleep-tracking wearables, or consumer sleep technologies (CSTs), is steadily increasing. CST marketing campaigns often advertise the scientific merit of devices, but these claims may not align with consensus opinion from sleep resea...

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  • Article
  • Open Access
16 Citations
6,813 Views
30 Pages
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Evaluation and Selection Models for Ensemble Intrusion Detection Systems in IoT

  • Rubayyi Alghamdi and
  • Martine Bellaiche
IoT2022, 3(2), 285-314;https://doi.org/10.3390/iot3020017

28 April 2022

Using the Internet of Things (IoT) for various applications, such as home and wearables devices, network applications, and even self-driven vehicles, detecting abnormal traffic is one of the problematic areas for researchers to protect network infras...

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  • Review
  • Open Access
28 Citations
6,359 Views
12 Pages
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On the Performance of Federated Learning Algorithms for IoT

  • Mehreen Tahir and
  • Muhammad Intizar Ali
IoT2022, 3(2), 273-284;https://doi.org/10.3390/iot3020016

22 April 2022

Federated Learning (FL) is a state-of-the-art technique used to build machine learning (ML) models based on distributed data sets. It enables In-Edge AI, preserves data locality, protects user data, and allows ownership. These characteristics of FL m...

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