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Electronics
Special Issues
Intelligence/Security Embedded IoT Systems
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Intelligence/Security Embedded IoT Systems

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 11635

Special Issue Editors

Prof. Dr. Il-Gu Lee

E-Mail Website
Guest Editor
Department of Convergence Security Engineering, Sungshin University, Seoul 02844, Korea
Interests: information security; communications and networks; IoT; security and privacy; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kyungmin Go

E-Mail Website
Guest Editor
Department of Computer Engineering, Pai Chai University, Daejeon 35345, Korea
Interests: self-driving/connected car networks; secure video transmission; deep reinforcement learning; mobile computing; system software testing
Dr. Jung Hoon Lee

E-Mail Website
Guest Editor
Department of Electronics Engineering, Hankuk University of Foreign Studies, Yongin 17035, Republic of Korea
Interests: wireless security; machine learning; interference management; channel quantization; game theory; signal processing techniques in wireless communication systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The pervasiveness of IoT technologies in a variety of fields is changing our lives. Industry, agriculture, health, and even personal life are experiencing a revolution in the way data are collected, communicated, processed, and stored.

This Special Issue aims to promote discussions of research and relevant activities in the models and design of secure, privacy-preserving, or trust architectures, data analyses, and fusion platforms, protocols, algorithms, services, and applications for next-generation IoT systems.

This Special Issue invites researchers to contribute with original contributions, case studies, and reviews that address all new challenges due to the use of IoT in synergy with security and privacy-preserving techniques and/or machine learning techniques.

Topics of interest include, but are not limited to:

  • Machine learning based security, privacy, and trust issues in IoT
  • Security and privacy frameworks for IoT at home
  • Threat and attack model generation based on machine learning for IoT
  • Machine learning based intrusion and malware detection for IoT
  • End-to-end system security models for IoT
  • Data privacy and device security techniques for IoT
  • Cryptographic approaches for security and privacy in IoT
  • Architectures and protocols for scalable, secure, robust and privacy enhancing IoT
  • Deep Learning for IoT
  • Machine learning for deep packet inspection for IoT
  • Machine learning to analyze cryptographic protocols for IoT
  • Novel machine learning and data science methods for IoT security
  • Data mining and statistical modeling for the secure IoT
  • Adversarial machine learning for IoT
  • Data based metrics and risk assessment approaches for IoT
  • Machine learning based authentication and access control in IoT
  • New concepts and architectures for intelligent blockchain for IoT
  • AI-enabled scalable blockchain for IoT
  • Security and privacy of blockchain-based IoT
  • Blockchain for wireless IoT networks
  • Intelligent blockchain driven IoT applications
  • In/Out vehicle network security for IoT services
  • Secure intelligent transport systems for future IoT services

Prof. Dr. Il-Gu Lee
Prof. Dr. Kyungmin Go
Prof. Dr. Jung Hoon Lee
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

  • IoT
  • Security
  • Privacy
  • Intelligence
  • Machine learning
  • Artificial intelligence
  • Blockchain

Published Papers (3 papers)

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Research

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15 pages, 6061 KiB  
Article
Vibration Prediction of Flying IoT Based on LSTM and GRU
by Jun-Ki Hong
Electronics 2022, 11(7), 1052; https://doi.org/10.3390/electronics11071052 - 27 Mar 2022
Cited by 7 | Viewed by 1894
Abstract
Drones, flying Internet of Things (IoT), have been widely used in several industrial fields, including rescue, delivery, military, and agriculture. Motors connected to a drone’s propellers play a crucial role in its movement. However, once the motor is damaged, the drone is at [...] Read more.
Drones, flying Internet of Things (IoT), have been widely used in several industrial fields, including rescue, delivery, military, and agriculture. Motors connected to a drone’s propellers play a crucial role in its movement. However, once the motor is damaged, the drone is at risk of falling. Thus, to prevent the drone from falling, an accurate and reliable prediction of motor vibration is necessary. In this study, four types of time series vibration data collected in the time domain from motors are predicted using long short-term memory (LSTM) and gated recurrent unit (GRU), and the accuracy and time efficiency of the predicted and actual vibration waveforms are compared and examined. According to the simulation results, the coefficient of determination, R2, and the root mean square error values obtained from the actual and predicted vibrations by the LSTM and GRU are similar. Furthermore, both the LSTM and GRU show excellent performance in forecasting future motor vibration, but GRU can predict future vibration about 22.79% faster than LSTM. Full article
(This article belongs to the Special Issue Intelligence/Security Embedded IoT Systems)
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18 pages, 3931 KiB  
Article
Threat Analysis and Distributed Denial of Service (DDoS) Attack Recognition in the Internet of Things (IoT)
by Mohammed Hasan Ali, Mustafa Musa Jaber, Sura Khalil Abd, Amjad Rehman, Mazhar Javed Awan, Robertas Damaševičius and Saeed Ali Bahaj
Electronics 2022, 11(3), 494; https://doi.org/10.3390/electronics11030494 - 08 Feb 2022
Cited by 57 | Viewed by 4816
Abstract
The Internet of Things (IoT) plays a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although this IoT paradigm has suffered from intrusion threats and [...] Read more.
The Internet of Things (IoT) plays a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although this IoT paradigm has suffered from intrusion threats and attacks that cause security and privacy issues, existing intrusion detection techniques fail to maintain reliability against the attacks. Therefore, the IoT intrusion threat has been analyzed using the sparse convolute network to contest the threats and attacks. The web is trained using sets of intrusion data, characteristics, and suspicious activities, which helps identify and track the attacks, mainly, Distributed Denial of Service (DDoS) attacks. Along with this, the network is optimized using evolutionary techniques that identify and detect the regular, error, and intrusion attempts under different conditions. The sparse network forms the complex hypotheses evaluated using neurons, and the obtained event stream outputs are propagated to further hidden layer processes. This process minimizes the intrusion involvement in IoT data transmission. Effective utilization of training patterns in the network successfully classifies the standard and threat patterns. Then, the effectiveness of the system is evaluated using experimental results and discussion. Network intrusion detection systems are superior to other types of traditional network defense in providing network security. The research applied an IGA-BP network to combat the growing challenge of Internet security in the big data era, using an autoencoder network model and an improved genetic algorithm to detect intrusions. MATLAB built it, which ensures a 98.98% detection rate and 99.29% accuracy with minimal processing complexity, and the performance ratio is 90.26%. A meta-heuristic optimizer was used in the future to increase the system’s ability to forecast attacks. Full article
(This article belongs to the Special Issue Intelligence/Security Embedded IoT Systems)
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Review

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28 pages, 3403 KiB  
Review
Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
by Raniyah Wazirali, Rami Ahmad, Ahmed Al-Amayreh, Mohammad Al-Madi and Ala’ Khalifeh
Electronics 2021, 10(14), 1744; https://doi.org/10.3390/electronics10141744 - 20 Jul 2021
Cited by 26 | Viewed by 4271
Abstract
Information security is considered one of the most important issues in various infrastructures related to the field of data communication where most of the modern studies focus on finding effective and low-weight secure approaches. Digital watermarking is a trend in security techniques that [...] Read more.
Information security is considered one of the most important issues in various infrastructures related to the field of data communication where most of the modern studies focus on finding effective and low-weight secure approaches. Digital watermarking is a trend in security techniques that hides data by using data embedding and data extraction processes. Watermarking technology is integrated into different frames without adding an overheard as in the conventional encryption. Therefore, it is efficient to be used in data encryption for applications that run over limited resources such as the Internet of Things (IoT). In this paper, different digital watermarking algorithms and approaches are presented. Additionally, watermarking requirements and challenges are illustrated in detail. Moreover, the common architecture of the watermarking system is described. Furthermore, IoT technology and its challenges are highlighted. Finally, the paper provides the motivations, objectives and applications of the recent secure watermarking techniques in IoT and summarises them into one table. In addition, the paper highlights the potential to apply the modified watermark algorithms to secure IoT networks. Full article
(This article belongs to the Special Issue Intelligence/Security Embedded IoT Systems)
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