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Recent Trends and Advances in IoT Security
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Recent Trends and Advances in IoT Security

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1647

Special Issue Editors

Prof. Dr. Nicola Dragoni

E-Mail Website
Guest Editor
DTU Compute, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, 2800 Kongens Lyngby, Denmark
Interests: pervasive computing; IoT; cyber security; blockchain technologies; fog computing; cryptocurrency technologies; mobile computing; microservices
Dr. Gaurav Choudhary

E-Mail Website
Guest Editor
DTU Compute, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, 2800 Kongens Lyngby, Denmark
Interests: threat intelligence; IoT and CPS security; cyber security; vulnerability assessment; 5G security; drone security; cryptography
Special Issues, Collections and Topics in MDPI journals
Dr. Edlira Dushku

E-Mail Website
Guest Editor
Department of Electronic Systems, Aalborg University, Frederikskaj 12, 2450 Copenhagen, Denmark
Interests: cybersecurity; security and privacy; trusted computing; remote attestation; Internet of Things (IoT); fog computing

Special Issue Information

Dear Colleagues,

With the recent Internet of Things (IoT) revolution, IoT devices are continuously being deployed at a tremendous pace in various domains ranging from industrial systems to consumer IoT products. Thus, IoT capabilities in data generation, collection, and analysis are leading towards smart systems in healthcare, transportation and the environment, among many other domains. Moreover, the integration of the emerging IoT application with Artificial Intelligence (AI) ignites radical digital transformation of IoT–Fog–Cloud environments. However, along with unprecedented opportunities, this enormous growth exposes IoT devices to many cybersecurity threats. To deal with such an ever-expanding attack surface, IoT systems demand cutting-edge approaches to prevent, detect and heal compromised IoT devices from different cyberattacks. In this context, the development of new threat analytics, risk management and self-healing capabilities play a crucial role in detecting and defeating potential attacks. In addition, future IoT security systems should quickly and appropriately respond to threats and attacks, incorporate and learn from new threat information and develop and enact thread mitigation plans.

This Special Issue expects innovative work to explore new frontiers and challenges in the field of Cybersecurity and Edge–Cloud IoT research, including IoT cybersecurity frameworks, next-generation research requirements, IoT cybersecurity architectures, scalable security solutions, critical enabling countermeasures and strategies, major industry applications and research trends and challenges.

The list of possible topics includes, but is not limited to, the following:

  • Security, privacy and safety for IoT;
  • Secure IoT architectures and framework;
  • Edge–Cloud computing technologies, services and applications for a secure IoT;
  • Cyber physical systems security;
  • Artificial Intelligence for security;
  • Privacy-preserving machine learning techniques for Edge–Cloud IoT;
  • Remote attestation for IoT-toFog environments;
  • Intrusion detection on IoT Reliability;
  • Disaster recovery in IoT;
  • Malware detection;
  • Machine learning for secure IoT;
  • Adversarial machine leaning;
  • Lightweight/resource constrained IoT security frameworks and solutions;
  • Next-generation cyber security solutions and smart threat detection for IoT;
  • Advanced misbehavior detection solutions in IoT devices/networks;
  • Advance communication network (5G, 6G)-enabled IoT and its security;
  • Self-driven security solutions for IoT networks/systems;
  • Post-quantum computing for IoT.

Prof. Dr. Nicola Dragoni
Dr. Gaurav Choudhary
Dr. Edlira Dushku
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. Sensors 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 2600 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 and privacy
  • IoT safety
  • security and privacy by design security monitoring of IoT systems
  • secure deplovment of IoT systems
  • formal methods for secure and safe IoT systems
  • remote attestation
  • edge-fog-cloud architecture

Published Papers (1 paper)

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Research

20 pages, 2297 KiB  
Article
HOMLC-Hyperparameter Optimization for Multi-Label Classification of Intrusion Detection Data for Internet of Things Network
by Ankita Sharma, Shalli Rani, Dipak Kumar Sah, Zahid Khan and Wadii Boulila
Sensors 2023, 23(19), 8333; https://doi.org/10.3390/s23198333 - 09 Oct 2023
Cited by 3 | Viewed by 1019
Abstract
The comparison of low-rank-based learning models for multi-label categorization of attacks for intrusion detection datasets is presented in this work. In particular, we investigate the performance of three low-rank-based machine learning (LR-SVM) and deep learning models (LR-CNN), (LR-CNN-MLP) for classifying intrusion detection data: [...] Read more.
The comparison of low-rank-based learning models for multi-label categorization of attacks for intrusion detection datasets is presented in this work. In particular, we investigate the performance of three low-rank-based machine learning (LR-SVM) and deep learning models (LR-CNN), (LR-CNN-MLP) for classifying intrusion detection data: Low Rank Representation (LRR) and Non-negative Low Rank Representation (NLR). We also look into how these models’ performance is affected by hyperparameter tweaking by using Guassian Bayes Optimization. The tests has been run on merging two intrusion detection datasets that are available to the public such as BoT-IoT and UNSW- NB15 and assess the models’ performance in terms of key evaluation criteria, including precision, recall, F1 score, and accuracy. Nevertheless, all three models perform noticeably better after hyperparameter modification. The selection of low-rank-based learning models and the significance of the hyperparameter tuning log for multi-label classification of intrusion detection data have been discussed in this work. A hybrid security dataset is used with low rank factorization in addition to SVM, CNN and CNN-MLP. The desired multilabel results have been obtained by considering binary and multi-class attack classification as well. Low rank CNN-MLP achieved suitable results in multilabel classification of attacks. Also, a Gaussian-based Bayesian optimization algorithm is used with CNN-MLP for hyperparametric tuning and the desired results have been achieved using c and γ for SVM and α and β for CNN and CNN-MLP on a hybrid dataset. The results show the label UDP is shared among analysis, DoS and shellcode. The accuracy of classifying UDP among three classes is 98.54%. Full article
(This article belongs to the Special Issue Recent Trends and Advances in IoT Security)
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