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Electronics
Special Issues
Recent Advances in AI-Enabled Internet of Things Security and Privacy
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Recent Advances in AI-Enabled Internet of Things Security and Privacy

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 2294

Special Issue Editors

Prof. Dr. Insoo Sohn

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Guest Editor
Department of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Korea
Interests: neural network; network security; complex network; wireless network; medical informatics
Prof. Dr. Huaping Liu

E-Mail Website1 Website2
Guest Editor
School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USA
Interests: wireless communication; localization; energy harvesting; machine learning
Prof. Dr. Tae (Tom) Oh

E-Mail Website1 Website2
Guest Editor
Golisano College of Computing and Information Sciences, Rochester Institute of Technology, New York, NY 14623, USA
Interests: vehicular area networks; data acquisition and analytics; mobile device security; IoT security

Special Issue Information

Dear Colleagues,

We are experiencing explosive growth in digital data and connected devices today based on the successful realization of the Internet of Things (IoT). The application of artificial intelligence (AI) and machine learning (ML) is prevalent in a wide range of fields. In recent years, AI and ML techniques have been gaining importance as tools to provide intelligence to the IoT. With increased intelligence, the goal of AI-enabled IoT is to provide better heterogeneous connectivity, ubiquitous coverage, reduced network and device complexity, enhanced power savings, and enhanced resource management.

However, due to the deployment of AI-based intelligence for IoT applications, a new set of problems, challenges, risks, and vulnerabilities have been introduced. Thus, it is important to address the challenges provided by intelligent cyberattacks that are targeted towards AI-based IoT.

Prospective authors are invited to submit original manuscripts on topics including, but not limited to:

  • Security and privacy issues in intelligent industrial IoT
  • Security and privacy issues in intelligent medical IoT
  • Security and privacy issues in intelligent smart city IoT
  • Security and privacy issues in vehicular IoT
  • Security and privacy issues of blockchain technologies for AI-enabled IoT
  • Security and privacy issues in intelligent edge network
  • Attack models for AI-enabled IoT systems
  • Adversarial learning in AI-enabled IoT systems
  • AI-based intrusion detection in IoT systems
  • Federated learning-based security techniques in intelligent IoT systems
  • Experimental testbeds for testing security in AI-enabled IoT systems

Prof. Dr. Insoo Sohn
Prof. Dr. Huaping Liu
Prof. Dr. Tae (Tom) Oh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  •  Security and Privacy in IoT
  •  Intelligent IoT
  •  Industrial IoT
  •  Medical IoT
  •  Smart City with IoT
  •  Vehicular IoT
  •  Intelligent Edge Network
  •  Adversarial Learning
  •  Federated Learning
  •  Security in Blockchain

Published Papers (1 paper)

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Research

16 pages, 2250 KiB  
Article
Open Set Recognition for Malware Traffic via Predictive Uncertainty
by Xue Li, Jinlong Fei, Jiangtao Xie, Ding Li, Heng Jiang, Ruonan Wang and Zan Qi
Electronics 2023, 12(2), 323; https://doi.org/10.3390/electronics12020323 - 8 Jan 2023
Cited by 1 | Viewed by 1354
Abstract
Existing machine learning-based malware traffic recognition techniques can effectively detect abnormal behaviors in the network. However, almost all of them focus on a closed-set scenario in which the data used for training and testing come from the same label space. Since sophisticated malware [...] Read more.
Existing machine learning-based malware traffic recognition techniques can effectively detect abnormal behaviors in the network. However, almost all of them focus on a closed-set scenario in which the data used for training and testing come from the same label space. Since sophisticated malware and advanced persistent threats are evolving, it is impossible to exhaust all attacks to train a complete recognition model under the existing technical conditions. Therefore, recognition in the real network is an open-set problem, i.e., the recognition system should identify unknown and unseen attacks at test time. In this paper, we propose an uncertainty-aware method to identify known malicious traffic accurately and handle unknown traffic effectively. This method employs predictive uncertainty in deep learning as an indicator for unknown class detection. The predictive uncertainty represents the confidence in neural network predictions. In particular, the Deep Evidence Malware Traffic Recognition (DEMTR) model is presented to provide the multi-classification probability and predictive uncertainty in open-set scenarios using evidential deep learning. We demonstrate the performance of DEMTR on the MCFP dataset. Experimental results indicate that the proposed model outperforms the baseline methods in accuracy and F1-score. Full article
(This article belongs to the Special Issue Recent Advances in AI-Enabled Internet of Things Security and Privacy)
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