Cyber Security in IoT

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 8987

Special Issue Editors


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Guest Editor
Institute of Telecommunications, Warsaw University of Technology, 00-665 Warszawa, Poland
Interests: cybersecurity; digital forensics; steganography; anomaly detection
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute of Telecommunications, Warsaw University of Technology, 00-665 Warszawa, Poland
Interests: cybersecurity; IoT; education

Special Issue Information

Dear Colleagues,

In 2021, the number of active IoT devices amounted to 12.2 trillion. It is predicted that by 2025 there will be 27 trillion active IoT devices. Smart devices connected to the Internet make our lives more comfortable and enjoyable. As consumers, we like and need to exchange information with others in real-time. Smartphones, smart watches, smart home appliances, smart cars and intelligent cities blend more and more into our everyday life. Companies, enterprises or device manufacturers control industrial processes or supply chains using an increasing number of devices connected to the Internet called industrial control systems. Systems or services based on the IoT ecosystem are becoming critical for organisations and consumers from the point of view of their business continuity. They should be included in risk management systems. The IoT ecosystem comprises elements that make up IoT solutions and allow you to manage IoT devices. The most important aspects of the IoT ecosystem are IoT hardware solutions consisting of sensors, microcontrollers, microprocessors, power systems, memory and communication interfaces; IoT device software along with authorisation and data protection services; wired and wireless communication networks; platforms collecting data integrated with cloud services; data processing algorithms, including artificial intelligence and machine learning algorithms, as well as personal teams dealing with data processing, scientists, IT specialists and cybersecurity specialists. Each of these elements of the IoT ecosystem can be an area vulnerable to cybercriminal attacks. Unauthorised access to a specific layer of the IoT ecosystem may cause visible or invisible effects on the services provided. In particular, it may threaten business continuity or drive the incorrect implementation of processes. This Special Issue is dedicated to presenting threats, vulnerabilities to cybercriminals' attacks and proactive and reactive solutions to protecting individual elements of the ecosystem of intelligent devices connected to the Internet.

Prof. Dr. Krzysztof Szczypiorski
Dr. Daniel Paczesny
Guest Editors

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Keywords

  • IoT cybersecurity prevention and response strategies
  • emergent cybersecurity risks arising from IoT-enabled 5G and 6G
  • situation awareness of IoT environment
  • risk identification, assessment, and mitigation in IoT systems
  • security architecture and frameworks for IoT
  • IoT devices and protocols security
  • attack detection and prevention in IoT
  • privacy-preserving techniques in IoT
  • secure integration of IoT and cloud/edge computing
  • machine learning techniques for IoT security
  • secure data management approaches
  • security in cyber-physical systems
  • blockchain technologies for reliable and trustworthy IoT
  • threat and vulnerability analysis of IoT

Published Papers (4 papers)

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Research

28 pages, 4291 KiB  
Article
Cyber Security on the Edge: Efficient Enabling of Machine Learning on IoT Devices
by Swati Kumari, Vatsal Tulshyan and Hitesh Tewari
Information 2024, 15(3), 126; https://doi.org/10.3390/info15030126 - 23 Feb 2024
Viewed by 1548
Abstract
Due to rising cyber threats, IoT devices’ security vulnerabilities are expanding. However, these devices cannot run complicated security algorithms locally due to hardware restrictions. Data must be transferred to cloud nodes for processing, giving attackers an entry point. This research investigates distributed computing [...] Read more.
Due to rising cyber threats, IoT devices’ security vulnerabilities are expanding. However, these devices cannot run complicated security algorithms locally due to hardware restrictions. Data must be transferred to cloud nodes for processing, giving attackers an entry point. This research investigates distributed computing on the edge, using AI-enabled IoT devices and container orchestration tools to process data in real time at the network edge. The purpose is to identify and mitigate DDoS assaults while minimizing CPU usage to improve security. It compares typical IoT devices with and without AI-enabled chips, container orchestration, and assesses their performance in running machine learning models with different cluster settings. The proposed architecture aims to empower IoT devices to process data locally, minimizing the reliance on cloud transmission and bolstering security in IoT environments. The results correlate with the update in the architecture. With the addition of AI-enabled IoT device and container orchestration, there is a difference of 60% between the new architecture and traditional architecture where only Raspberry Pi were being used. Full article
(This article belongs to the Special Issue Cyber Security in IoT)
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23 pages, 4164 KiB  
Article
Security Awareness in Smart Homes and Internet of Things Networks through Swarm-Based Cybersecurity Penetration Testing
by Thomas Schiller, Bruce Caulkins, Annie S. Wu and Sean Mondesire
Information 2023, 14(10), 536; https://doi.org/10.3390/info14100536 - 30 Sep 2023
Viewed by 2765
Abstract
Internet of Things (IoT) devices are common in today’s computer networks. These devices can be computationally powerful, yet prone to cybersecurity exploitation. To remedy these growing security weaknesses, this work proposes a new artificial intelligence method that makes these IoT networks safer through [...] Read more.
Internet of Things (IoT) devices are common in today’s computer networks. These devices can be computationally powerful, yet prone to cybersecurity exploitation. To remedy these growing security weaknesses, this work proposes a new artificial intelligence method that makes these IoT networks safer through the use of autonomous, swarm-based cybersecurity penetration testing. In this work, the introduced Particle Swarm Optimization (PSO) penetration testing technique is compared against traditional linear and queue-based approaches to find vulnerabilities in smart homes and IoT networks. To evaluate the effectiveness of the PSO approach, a network simulator is used to simulate smart home networks of two scales: a small, home network and a large, commercial-sized network. These experiments demonstrate that the swarm-based algorithms detect vulnerabilities significantly faster than the linear algorithms. The presented findings support the case that autonomous and swarm-based penetration testing in a network could be used to render more secure IoT networks in the future. This approach can affect private households with smart home networks, settings within the Industrial Internet of Things (IIoT), and military environments. Full article
(This article belongs to the Special Issue Cyber Security in IoT)
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26 pages, 10485 KiB  
Article
Defending IoT Devices against Bluetooth Worms with Bluetooth OBEX Proxy
by Fu-Hau Hsu, Min-Hao Wu, Yan-Ling Hwang, Jian-Xin Chen, Jian-Hong Huang, Hao-Jyun Wang and Yi-Wen Lai
Information 2023, 14(10), 525; https://doi.org/10.3390/info14100525 - 27 Sep 2023
Viewed by 1601
Abstract
The number of Internet of Things (IoT) devices has increased dramatically in recent years, and Bluetooth technology is critical for communication between IoT devices. It is possible to protect electronic communications, the Internet of Things (IoT), and big data from malware and data [...] Read more.
The number of Internet of Things (IoT) devices has increased dramatically in recent years, and Bluetooth technology is critical for communication between IoT devices. It is possible to protect electronic communications, the Internet of Things (IoT), and big data from malware and data theft with BlueZ’s Bluetooth File Transfer Filter (BTF). It can use a configurable filter to block unauthorized Bluetooth file transfers. The BTF is available for various Linux distributions and can protect many Bluetooth-enabled devices, including smartphones, tablets, laptops, and the Internet of Things. However, the increased number and density of Bluetooth devices have also created a serious problem—the Bluetooth worm. It poses a severe threat to the security of Bluetooth devices. In this paper, we propose a Bluetooth OBEX Proxy (BOP) to filter malicious files transferred to devices via the OBEX system service in BlueZ. The method described in this article prevents illegal Bluetooth file transfers, defending big data, the Internet of Things (IoT), and electronic communications from malware and data theft. It also protects numerous Bluetooth devices, including smartphones, tablets, laptops, and the Internet of Things, with many Linux distributions. Overall, the detection findings were entirely accurate, with zero false positives and 2.29% misses. Full article
(This article belongs to the Special Issue Cyber Security in IoT)
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16 pages, 2129 KiB  
Article
Localization of False Data Injection Attack in Smart Grids Based on SSA-CNN
by Kelei Shen, Wenxu Yan, Hongyu Ni and Jie Chu
Information 2023, 14(3), 180; https://doi.org/10.3390/info14030180 - 14 Mar 2023
Cited by 5 | Viewed by 1772
Abstract
In recent years, smart grids have integrated information and communication technologies into power networks, which brings new network security issues. Among the existing cyberattacks, the false data injection attack (FDIA) compromises state estimation in smart grids by injecting false data into the meter [...] Read more.
In recent years, smart grids have integrated information and communication technologies into power networks, which brings new network security issues. Among the existing cyberattacks, the false data injection attack (FDIA) compromises state estimation in smart grids by injecting false data into the meter measurements, which adversely affects the smart grids. Current studies on FDIAs mainly focus on the detection of its existence, but there are few studies on its localization. Most attack localization methods have difficulty locating the specific bus or line that is under attack quickly and accurately, have high computational complexity and are difficult to apply to large power networks. Therefore, this paper proposes a localization method for FDIAs that is based on a convolutional neural network and optimized with a sparrow search algorithm (SSA–CNN). Based on the physical meaning of measurement vectors, the proposed method can precisely locate a specific bus or line with relatively low computational complexity. To address the difficulty of selecting hyperparameters in the CNN, which leads to the degradation of localization accuracy, a SSA is used to optimize the hyperparameters of the CNN so that the hyperparameters are optimal when using the model for localization. Finally, simulation experiments are conducted on IEEE14-bus and IEEE118-bus test systems, and the simulation results show that the method proposed in this paper has a high localization accuracy and can largely reduce the false-alarm rate. Full article
(This article belongs to the Special Issue Cyber Security in IoT)
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