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Cybersecurity and Reliability for 5G and Beyond and IoT Applications

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

Deadline for manuscript submissions: 10 November 2024 | Viewed by 15782

Special Issue Editors


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Guest Editor
Norsk Regnesentral (Norwegian Computing Center, NR), 0373 Oslo, Norway
Interests: adaptive security; cybersecurity; Internet of Things; context-awareness; game theory; WBANs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There has been exponential growth in deploying the Internet of Things (IoT) in various applications, including healthcare, transportation, defense, industry, etc. The 5G and beyond networks will allow IoT devices to interconnect and share data faster than ever. The advantages of 5G and beyond are the unique combination of high-speed connectivity, low latency, and more effectiveness and efficiency than its predecessor technologies. The 5G-and-beyond-enabled IoT communication faces different cybersecurity and reliability issues. Therefore, cybersecurity and reliability are paramount requirements for 5G and beyond and IoT applications. These applications are also vulnerable to different types of possible attacks, including distributed denial-of-service, malicious routing, password reckoning, malware, man-in-the-middle, among others. Therefore, it is critical to protect 5G and beyond and IoT communication and infrastructures.

This Special Issue seeks the latest contributions and reviews offering cybersecurity and reliability solutions for 5G and beyond and IoT applications. The topics of interest in 5G and beyond and IoT domains include, but are not limited to:

  • Networking and communication security and reliability;
  • Cyberattack defense and vulnerability modeling;
  • Architectures, guidelines, and standards for cybersecurity and reliability;
  • Secure and reliable information management;
  • Methods for addressing the unreliability of local devices;
  • Digital twins for enhancing cybersecurity;
  • ML- and AI-enabled critical infrastructure protection;
  • Adaptive security solutions;
  • Software testing approaches for reliable communication;
  • Decentralized schemes for trust management;
  • Autonomous security and service management;
  • Security assurance and optimization;
  • Big data security and reliability.

Dr. Sandeep Pirbhulal
Dr. Habtamu Abie
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.

Published Papers (7 papers)

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Research

15 pages, 505 KiB  
Article
Detecting DoS Attacks through Synthetic User Behavior with Long Short-Term Memory Network
by Patrycja Nędza and Jerzy Domżał
Sensors 2024, 24(12), 3735; https://doi.org/10.3390/s24123735 - 8 Jun 2024
Viewed by 219
Abstract
With the escalation in the size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of [...] Read more.
With the escalation in the size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of ML in generating behavioral telemetry data using Long Short-Term Memory network and spoofing requests for the analyzed traffic to look legitimate. For this research, a custom testing environment was built that listens for mouse and keyboard events and analyzes them accordingly. While the economic feasibility of this attack currently limits its immediate threat, advancements in technology could make it more cost-effective for attackers in the future. Therefore, proactive development of countermeasures remains essential to mitigate potential risks and stay ahead of evolving attack methods. Full article
(This article belongs to the Special Issue Cybersecurity and Reliability for 5G and Beyond and IoT Applications)
19 pages, 1432 KiB  
Article
Poisoning Attacks against Communication and Computing Task Classification and Detection Techniques
by Younes Salmi and Hanna Bogucka
Sensors 2024, 24(2), 338; https://doi.org/10.3390/s24020338 - 5 Jan 2024
Viewed by 886
Abstract
Machine learning-based classification algorithms allow communication and computing (2C) task offloading from the end devices to the edge computing network servers. In this paper, we consider task classification based on the hybrid k-means and k-nearest neighbors algorithms. Moreover, we examine the [...] Read more.
Machine learning-based classification algorithms allow communication and computing (2C) task offloading from the end devices to the edge computing network servers. In this paper, we consider task classification based on the hybrid k-means and k-nearest neighbors algorithms. Moreover, we examine the poisoning attacks on such ML algorithms, namely noise-like jamming and targeted data feature falsification, and their impact on the effectiveness of 2C task allocation. Then, we also present two anomaly detection methods using noise training and the silhouette score test to detect the poisoned samples and mitigate their impact. Our simulation results show that these attacks have a fatal effect on classification in feature areas where the decision boundary is unclear. They also demonstrate the effectiveness of our countermeasures against the considered attacks. Full article
(This article belongs to the Special Issue Cybersecurity and Reliability for 5G and Beyond and IoT Applications)
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21 pages, 2846 KiB  
Article
On the Security of a PUF-Based Authentication and Key Exchange Protocol for IoT Devices
by Da-Zhi Sun, Yi-Na Gao and Yangguang Tian
Sensors 2023, 23(14), 6559; https://doi.org/10.3390/s23146559 - 20 Jul 2023
Cited by 2 | Viewed by 1761
Abstract
Recently, Roy et al. proposed a physically unclonable function (PUF)-based authentication and key exchange protocol for Internet of Things (IoT) devices. The PUF protocol is efficient, because it integrates both the Node-to-Node (N2N) authentication and the Node-to-Server (N2S) authentication into a standalone protocol. [...] Read more.
Recently, Roy et al. proposed a physically unclonable function (PUF)-based authentication and key exchange protocol for Internet of Things (IoT) devices. The PUF protocol is efficient, because it integrates both the Node-to-Node (N2N) authentication and the Node-to-Server (N2S) authentication into a standalone protocol. In this paper, we therefore examine the security of the PUF protocol under the assumption of an insider attack. Our cryptanalysis findings are the following. (1) A legitimate but malicious IoT node can monitor the secure communication among the server and any other IoT nodes in both N2N authentication and N2S authentication. (2) A legitimate but malicious IoT node is able to impersonate a target IoT node to cheat the server and any other IoT nodes in N2N authentication and the server in N2S authentication, respectively. (3) A legitimate but malicious IoT node can masquerade as the server to cheat any other target IoT nodes in both N2N authentication and N2S authentication. To the best of our knowledge, our work gives the first non-trivial concrete security analysis for the PUF protocol. In addition, we employ the automatic verification tool of security protocols, i.e., Scyther, to confirm the weaknesses found in the PUF protocol. We finally consider how to prevent weaknesses in the PUF protocol. Full article
(This article belongs to the Special Issue Cybersecurity and Reliability for 5G and Beyond and IoT Applications)
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19 pages, 3803 KiB  
Article
Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology
by Jiwoon Yoo and Yonghyun Jo
Sensors 2023, 23(11), 5033; https://doi.org/10.3390/s23115033 - 24 May 2023
Cited by 3 | Viewed by 2782
Abstract
Artificial intelligence (AI) technology is crucial for developing autonomous ships in the maritime industry. Autonomous ships, based on the collected information, recognize the environment without any human intervention and operate themselves using their own judgment. However, ship-to-land connectivity increased, owing to the real-time [...] Read more.
Artificial intelligence (AI) technology is crucial for developing autonomous ships in the maritime industry. Autonomous ships, based on the collected information, recognize the environment without any human intervention and operate themselves using their own judgment. However, ship-to-land connectivity increased, owing to the real-time monitoring and remote control (for unexpected circumstances) from land; this poses a potential cyberthreat to various data collected inside and outside the ships and to the applied AI technology. For the safety of autonomous ships, cybersecurity around AI technology needs to be considered, in addition to the cybersecurity of the ship systems. By identifying various vulnerabilities and via research cases of the ship systems and AI technologies, this study presents possible cyberattack scenarios on the AI technologies applied to autonomous ships. Based on these attack scenarios, cyberthreats and cybersecurity requirements are formulated for autonomous ships by employing the security quality requirements engineering (SQUARE) methodology. Full article
(This article belongs to the Special Issue Cybersecurity and Reliability for 5G and Beyond and IoT Applications)
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14 pages, 1698 KiB  
Article
Detection and Mitigation of SYN Flooding Attacks through SYN/ACK Packets and Black/White Lists
by Chun-Hao Yang, Jhen-Ping Wu, Fang-Yi Lee, Ting-Yu Lin and Meng-Hsun Tsai
Sensors 2023, 23(8), 3817; https://doi.org/10.3390/s23083817 - 7 Apr 2023
Viewed by 3798
Abstract
Software-defined networking (SDN) is a new network architecture that provides programmable networks, more efficient network management, and centralized control than traditional networks. The TCP SYN flooding attack is one of the most aggressive network attacks that can seriously degrade network performance. This paper [...] Read more.
Software-defined networking (SDN) is a new network architecture that provides programmable networks, more efficient network management, and centralized control than traditional networks. The TCP SYN flooding attack is one of the most aggressive network attacks that can seriously degrade network performance. This paper proposes detection and mitigation modules against SYN flooding attacks in SDN. We combine those modules, which have evolved from the cuckoo hashing method and innovative whitelist, to get better performance compared to current methods Our approach reduces the traffic through the switch and improves detection accuracy, also the required register size is reduced by half for the same accuracy. Full article
(This article belongs to the Special Issue Cybersecurity and Reliability for 5G and Beyond and IoT Applications)
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16 pages, 2641 KiB  
Article
Peer-to-Peer User Identity Verification Time Optimization in IoT Blockchain Network
by Ammar Riadh Kairaldeen, Nor Fadzilah Abdullah, Asma Abu-Samah and Rosdiadee Nordin
Sensors 2023, 23(4), 2106; https://doi.org/10.3390/s23042106 - 13 Feb 2023
Cited by 8 | Viewed by 2198
Abstract
Blockchain introduces challenges related to the reliability of user identity and identity management systems; this includes detecting unfalsified identities linked to IoT applications. This study focuses on optimizing user identity verification time by employing an efficient encryption algorithm for the user signature in [...] Read more.
Blockchain introduces challenges related to the reliability of user identity and identity management systems; this includes detecting unfalsified identities linked to IoT applications. This study focuses on optimizing user identity verification time by employing an efficient encryption algorithm for the user signature in a peer-to-peer decentralized IoT blockchain network. To achieve this, a user signature-based identity management framework is examined by using various encryption techniques and contrasting various hash functions built on top of the Modified Merkle Hash Tree (MMHT) data structure algorithm. The paper presents the execution of varying dataset sizes based on transactions between nodes to test the scalability of the proposed design for secure blockchain communication. The results show that the MMHT data structure algorithm using SHA3 and AES-128 encryption algorithm gives the lowest execution time, offering a minimum of 36% gain in time optimization compared to other algorithms. This work shows that using the AES-128 encryption algorithm with the MMHT algorithm and SHA3 hash function not only identifies malicious codes but also improves user integrity check performance in a blockchain network, while ensuring network scalability. Therefore, this study presents the performance evaluation of a blockchain network considering its distinct types, properties, components, and algorithms’ taxonomy. Full article
(This article belongs to the Special Issue Cybersecurity and Reliability for 5G and Beyond and IoT Applications)
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17 pages, 1843 KiB  
Article
Prevention of Cyber Security with the Internet of Things Using Particle Swarm Optimization
by Hassan A. Alterazi, Pravin R. Kshirsagar, Hariprasath Manoharan, Shitharth Selvarajan, Nawaf Alhebaishi, Gautam Srivastava and Jerry Chun-Wei Lin
Sensors 2022, 22(16), 6117; https://doi.org/10.3390/s22166117 - 16 Aug 2022
Cited by 18 | Viewed by 2863
Abstract
High security for physical items such as intelligent machinery and residential appliances is provided via the Internet of Things (IoT). The physical objects are given a distinct online address known as the Internet Protocol to communicate with the network’s external foreign entities through [...] Read more.
High security for physical items such as intelligent machinery and residential appliances is provided via the Internet of Things (IoT). The physical objects are given a distinct online address known as the Internet Protocol to communicate with the network’s external foreign entities through the Internet (IP). IoT devices are in danger of security issues due to the surge in hacker attacks during Internet data exchange. If such strong attacks are to create a reliable security system, attack detection is essential. Attacks and abnormalities such as user-to-root (U2R), denial-of-service, and data-type probing could have an impact on an IoT system. This article examines various performance-based AI models to predict attacks and problems with IoT devices with accuracy. Particle Swarm Optimization (PSO), genetic algorithms, and ant colony optimization were used to demonstrate the effectiveness of the suggested technique concerning four different parameters. The results of the proposed method employing PSO outperformed those of the existing systems by roughly 73 percent. Full article
(This article belongs to the Special Issue Cybersecurity and Reliability for 5G and Beyond and IoT Applications)
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