Security and Communication Networks

Security, Trust, and Privacy in Machine Learning-Based Internet of Things


Publishing date
01 May 2021
Status
Closed
Submission deadline
18 Dec 2020

Lead Editor

1Technical University of Denmark, Copenhagen, Denmark

2Hong Kong Polytechnic University, Hong Kong

3Queen's University Belfast, Belfast, UK

4University of Aizu, Aizuwakamatsu, Japan

This issue is now closed for submissions.
More articles will be published in the near future.

Security, Trust, and Privacy in Machine Learning-Based Internet of Things

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Internet of Things (IoT) allows billions of devices in the physical world as well as virtual environments to exchange data with each other intelligently. For example, smartphones have become an important personal assistant and an indispensable part of people's everyday life and work.

Machine learning has now been widely applied to IoT in order to facilitate performance and efficiency, such as reinforcement learning and deep learning. However, machine learning also suffers many issues, which may threaten the security, trust, and privacy of IoT environments. Among these issues, adversarial learning is one major threat, in which attackers may try to fool the learning algorithm with particular training examples, and lead to a false result.

This Special Issue will focus on cutting-edge research from both academia and industry and aims to solicit original research and review articles with a particular emphasis on discussing the security, trust, and privacy challenges in machine learning-based IoT.

Potential topics include but are not limited to the following:

  • Machine learning-based intrusion detection
  • Privacy attacks including machine learning-based attacks
  • Secure data collection with machine learning-based IoT
  • IoT privacy and anonymity with machine learning - forensics techniques
  • Trust management with machine learning for IoT applications
  • Applications of machine learning in IoT security, trust, and privacy
  • Vulnerability assessment in machine learning-based IoT
  • Secure routing in machine learning-based IoT
  • Adversarial learning for IoT

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 6674325
  • - Research Article

Cost-Sensitive Approach to Improve the HTTP Traffic Detection Performance on Imbalanced Data

Wenmin Li | Sanqi Sun | ... | Yijie Shi
  • Special Issue
  • - Volume 2021
  • - Article ID 5536722
  • - Research Article

Towards a Statistical Model Checking Method for Safety-Critical Cyber-Physical System Verification

Jian Xie | Wenan Tan | ... | Zhiqiu Huang
  • Special Issue
  • - Volume 2021
  • - Article ID 9961342
  • - Research Article

A Hierarchical Approach for Advanced Persistent Threat Detection with Attention-Based Graph Neural Networks

Zitong Li | Xiang Cheng | ... | Bing Chen
  • Special Issue
  • - Volume 2021
  • - Article ID 9919030
  • - Research Article

An Adaptive Communication-Efficient Federated Learning to Resist Gradient-Based Reconstruction Attacks

Yanbin Li | Yue Li | ... | Shougang Ren
  • Special Issue
  • - Volume 2021
  • - Article ID 6631075
  • - Research Article

An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM

Guanyu Lu | Xiuxia Tian
  • Special Issue
  • - Volume 2021
  • - Article ID 6661954
  • - Research Article

Machine Learning-Based Stealing Attack of the Temperature Monitoring System for the Energy Internet of Things

Qiong Li | Liqiang Zhang | ... | Yonghang Tai
  • Special Issue
  • - Volume 2021
  • - Article ID 5593435
  • - Research Article

A Residual Learning-Based Network Intrusion Detection System

Jiarui Man | Guozi Sun
  • Special Issue
  • - Volume 2021
  • - Article ID 6670847
  • - Research Article

An Efficient Anonymous Communication Scheme to Protect the Privacy of the Source Node Location in the Internet of Things

Fengyin Li | Pei Ren | ... | Huiyu Zhou
  • Special Issue
  • - Volume 2021
  • - Article ID 6695304
  • - Research Article

Two-Party Secure Computation for Any Polynomial Function on Ciphertexts under Different Secret Keys

Bingbing Jiang
  • Special Issue
  • - Volume 2021
  • - Article ID 6696149
  • - Research Article

A Privacy-Preserving Caching Scheme for Device-to-Device Communications

Yuqing Zhong | Zhaohua Li | Liping Liao
Security and Communication Networks
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Acceptance rate31%
Submission to final decision85 days
Acceptance to publication42 days
CiteScore4.200
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.