Cyber Security and the Internet of Things (IoT): Theories, Approaches, Smart Applications, and New Trends

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Internet of Things (IoT)".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 3541

Special Issue Editors


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Guest Editor
Department of Computer & Information Sciences, Towson University, Towson, MD 21252, USA
Interests: robust and secure deep learning networks; cybersecurity; security and privacy in Internet of Things (IoT); smart transportation; smart city; and smart healthcare.
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Computer & Information Sciences, Towson University, Towson, MD 21252, USA
Interests: big data analytics; cybersecurity, and networking in cyber physical systems and IoT
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
College of Engineering, Virginia Commonwealth University, Richmond, VA E4249, USA
Interests: wireless communication and networking; artificial intelligence; cyber security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) was developed to enable numerous smart applications, such as smart cities, smart transportation, smart homes, smart grid, smart industries, etc. With the rapid growth in the number of heterogeneous IoT devices and their applications, cybersecurity in IoT faces new challenges and vulnerabilities. Applying traditional security protection measures, such as encryption, authentication, and access control, might be insufficient to address attacks on the IoT, e.g., Mirai Botnet, webcam hacks, distributed denial-of-service (DDoS) attacks, and other sophisticated attacks. Additionally, the intrinsic distributed / decentralized structure and heterogeneity of IoT devices and data bring new challenges in enabling secure IoT systems. New IoT applications developed under such an environment may have limitations in defending against various specially designed attacks. The corresponding security approaches and theoretical foundations have not been fully studied. On the other hand, the emergence of AI and the blockchain as powerful tools have transformed the scheme of attack and defense in current IoTs. The effective use of these tools to resist cyber attacks in heterogeneous IoT systems and enable their secure and effective applications represents a critical milestone for the future development of IoT technologies. Therefore, this Special Issue will focus on the wide range of discussions and studies focused on building secure IoT systems and applications. The issue solicits the latest research outcomes on attack defense approaches, secure smart applications, security analysis in IoT systems, and potential new trends, challenges, and opportunities in this field.

Dr. Qianlong Wang
Dr. Weixian Liao
Dr. Changqing Luo
Guest Editors

Manuscript Submission Information

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Keywords

  • Internet of Things (IoT)
  • security and vulnerability analysis
  • AI-driven secure IoT
  • blockchain-empowered IoT
  • distributed / decentralized systems
  • heterogenous data

Published Papers (2 papers)

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Research

26 pages, 659 KiB  
Article
A Comparison of Neural-Network-Based Intrusion Detection against Signature-Based Detection in IoT Networks
by Max Schrötter, Andreas Niemann and Bettina Schnor
Information 2024, 15(3), 164; https://doi.org/10.3390/info15030164 - 14 Mar 2024
Viewed by 1313
Abstract
Over the last few years, a plethora of papers presenting machine-learning-based approaches for intrusion detection have been published. However, the majority of those papers do not compare their results with a proper baseline of a signature-based intrusion detection system, thus violating good machine [...] Read more.
Over the last few years, a plethora of papers presenting machine-learning-based approaches for intrusion detection have been published. However, the majority of those papers do not compare their results with a proper baseline of a signature-based intrusion detection system, thus violating good machine learning practices. In order to evaluate the pros and cons of the machine-learning-based approach, we replicated a research study that uses a deep neural network model for intrusion detection. The results of our replicated research study expose several systematic problems with the used datasets and evaluation methods. In our experiments, a signature-based intrusion detection system with a minimal setup was able to outperform the tested model even under small traffic changes. Testing the replicated neural network on a new dataset recorded in the same environment with the same attacks using the same tools showed that the accuracy of the neural network dropped to 54%. Furthermore, the often-claimed advantage of being able to detect zero-day attacks could not be seen in our experiments. Full article
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17 pages, 948 KiB  
Article
Robust Multiagent Reinforcement Learning for UAV Systems: Countering Byzantine Attacks
by Jishu K. Medhi, Rui Liu, Qianlong Wang and Xuhui Chen
Information 2023, 14(11), 623; https://doi.org/10.3390/info14110623 - 19 Nov 2023
Viewed by 1601
Abstract
Multiple unmanned aerial vehicle (multi-UAV) systems have gained significant attention in applications, such as aerial surveillance and search and rescue missions. With the recent development of state-of-the-art multiagent reinforcement learning (MARL) algorithms, it is possible to train multi-UAV systems in collaborative and competitive [...] Read more.
Multiple unmanned aerial vehicle (multi-UAV) systems have gained significant attention in applications, such as aerial surveillance and search and rescue missions. With the recent development of state-of-the-art multiagent reinforcement learning (MARL) algorithms, it is possible to train multi-UAV systems in collaborative and competitive environments. However, the inherent vulnerabilities of multiagent systems pose significant privacy and security risks when deploying general and conventional MARL algorithms. The presence of even a single Byzantine adversary within the system can severely degrade the learning performance of UAV agents. This work proposes a Byzantine-resilient MARL algorithm that leverages a combination of geometric median consensus and a robust state update model to mitigate, or even eliminate, the influence of Byzantine attacks. To validate its effectiveness and feasibility, the authors include a multi-UAV threat model, provide a guarantee of robustness, and investigate key attack parameters for multiple UAV navigation scenarios. Results from the experiments show that the average rewards during a Byzantine attack increased by up to 60% for the cooperative navigation scenario compared with conventional MARL techniques. The learning rewards generated by the baseline algorithms could not converge during training under these attacks, while the proposed method effectively converged to an optimal solution, proving its viability and correctness. Full article
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