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Topical Collection "Cryptography and Security in IoT and Sensor Networks"

A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Sensor Networks".

Editor

Topical Collection Information

Dear Colleagues,

The rapid advancement of the Internet of Things (IoT) and sensor networks is improving our quality of life and leading to a leap into a new world. It is of paramount importance to address various security threats and attacks for the successful establishment of such technologies. Accordingly, focusing on the aspect of cryptography and security for IoT and sensor networks, it is necessary to provide solid as well as evident solutions as countermeasures.

This Topical Collection aims to bring together current state-of-the-art research and future directions for cryptography and security in the IoT and sensor networks. For such a goal, we cordially invite researchers and engineers from both academia and industry to submit their original and novel work for inclusion in this Topical Collection. Tutorial or survey papers are also welcome.

The topics related to this collection include but are not limited to:

  • Secure architecture and models in the IoT and sensor networks;
  • Authentication and authorization in the IoT and sensor networks;
  • Privacy, trust, and reliability in the IoT and sensor networks;
  • Risk/threat assessment and management in the IoT and sensor networks;
  • Block and stream ciphers in the IoT and sensor networks;
  • Public key cryptography and digital signature in the IoT and sensor networks;
  • Secure cryptographic protocols and applications in the IoT and sensor networks;
  • Formal security verification in the IoT and sensor networks;
  • Post-quantum cryptography in the IoT and sensor networks;
  • Intrusion detection and prevention in the IoT and sensor networks;
  • Network security in the IoT and sensor networks;
  • Mobile security in the IoT and sensor networks;
  • Software security for the IoT and sensor networks;
  • AI security for the IoT and sensor networks;
  • Blockchain security for the IoT and sensor networks;
  • Others and emerging new topics.

Dr. Ilsun You
Collection Editor

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 collection 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 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.

Published Papers (3 papers)

2022

Article
An Implementation of Trust Chain Framework with Hierarchical Content Identifier Mechanism by Using Blockchain Technology
Sensors 2022, 22(13), 4831; https://doi.org/10.3390/s22134831 - 26 Jun 2022
Viewed by 520
Abstract
Advances in information technology (IT) and operation technology (OT) accelerate the development of manufacturing systems (MS) consisting of integrated circuits (ICs), modules, and systems, toward Industry 4.0. However, the existing MS does not support comprehensive identity forensics for the whole system, limiting its [...] Read more.
Advances in information technology (IT) and operation technology (OT) accelerate the development of manufacturing systems (MS) consisting of integrated circuits (ICs), modules, and systems, toward Industry 4.0. However, the existing MS does not support comprehensive identity forensics for the whole system, limiting its ability to adapt to equipment authentication difficulties. Furthermore, the development of trust imposed during their crosswise collaborations with suppliers and other manufacturers in the supply chain is poorly maintained. In this paper, a trust chain framework with a comprehensive identification mechanism is implemented for the designed MS system, which is based and created on the private blockchain in conjunction with decentralized database systems to boost the flexibility, traceability, and identification of the IC-module-system. Practical implementations are developed using a functional prototype. First, the decentralized application (DApp) and the smart contracts are proposed for constructing the new trust chain under the proposed comprehensive identification mechanism by using blockchain technology. In addition, the blockchain addresses of IC, module, and system are automatically registered to InterPlanetary File System (IPFS), individually. In addition, their corresponding hierarchical CID (content identifier) values are organized by using Merkle DAG (Directed Acyclic Graph), which is employed via the hierarchical content identifier mechanism (HCIDM) proposed in this paper. Based on insights obtained from this analysis, the trust chain based on HCIDM can be applied to any MS system, for example, this trust chain could be used to prevent the counterfeit modules and ICs employed in the monitoring system of a semiconductor factory environment. The evaluation results show that the proposed scheme could work in practice under the much lower costs, compared to the public blockchain, with a total cost of 0.002094 Ether. Finally, this research is developed an innovation trust chain mechanism that could be provided the system-level security for any MS toward Industrial 4.0 in order to meet the requirements of both manufacturing innovation and product innovation in Sustainable Development Goals (SDGs). Full article
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Article
Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
Sensors 2022, 22(10), 3819; https://doi.org/10.3390/s22103819 - 18 May 2022
Viewed by 579
Abstract
The 5G networks aim to realize a massive Internet of Things (IoT) environment with low latency. IoT devices with weak security can cause Tbps-level Distributed Denial of Service (DDoS) attacks on 5G mobile networks. Therefore, interest in automatic network intrusion detection using machine [...] Read more.
The 5G networks aim to realize a massive Internet of Things (IoT) environment with low latency. IoT devices with weak security can cause Tbps-level Distributed Denial of Service (DDoS) attacks on 5G mobile networks. Therefore, interest in automatic network intrusion detection using machine learning (ML) technology in 5G networks is increasing. ML-based DDoS attack detection in a 5G environment should provide ultra-low latency. To this end, utilizing a feature-selection process that reduces computational complexity and improves performance by identifying features important for learning in large datasets is possible. Existing ML-based DDoS detection technology mostly focuses on DDoS detection learning models on the wired Internet. In addition, studies on feature engineering related to 5G traffic are relatively insufficient. Therefore, this study performed feature selection experiments to reduce the time complexity of detecting and analyzing large-capacity DDoS attacks in real time based on ML in a 5G core network environment. The results of the experiment showed that the performance was maintained and improved when the feature selection process was used. In particular, as the size of the dataset increased, the difference in time complexity increased rapidly. The experiments show that the real-time detection of large-scale DDoS attacks in 5G core networks is possible using the feature selection process. This demonstrates the importance of the feature selection process for removing noisy features before training and detection. As this study conducted a feature study to detect network traffic passing through the 5G core with low latency using ML, it is expected to contribute to improving the performance of the 5G network DDoS attack automation detection technology using AI technology. Full article
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Article
A Universal Detection Method for Adversarial Examples and Fake Images
Sensors 2022, 22(9), 3445; https://doi.org/10.3390/s22093445 - 30 Apr 2022
Viewed by 487
Abstract
Deep-learning technologies have shown impressive performance on many tasks in recent years. However, there are multiple serious security risks when using deep-learning technologies. For examples, state-of-the-art deep-learning technologies are vulnerable to adversarial examples that make the model’s predictions wrong due to some specific [...] Read more.
Deep-learning technologies have shown impressive performance on many tasks in recent years. However, there are multiple serious security risks when using deep-learning technologies. For examples, state-of-the-art deep-learning technologies are vulnerable to adversarial examples that make the model’s predictions wrong due to some specific subtle perturbation, and these technologies can be abused for the tampering with and forgery of multimedia, i.e., deep forgery. In this paper, we propose a universal detection framework for adversarial examples and fake images. We observe some differences in the distribution of model outputs for normal and adversarial examples (fake images) and train the detector to learn the differences. We perform extensive experiments on the CIFAR10 and CIFAR100 datasets. Experimental results show that the proposed framework has good feasibility and effectiveness in detecting adversarial examples or fake images. Moreover, the proposed framework has good generalizability for the different datasets and model structures. Full article
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