Physical Security and Safety of IoT Equipment: A Survey of Recent Advances and Opportunities

Yang, Xing; Shu, Lei; Liu, Ye; Hancke, Gerhard P.; Ferrag, Mohamed Amine; Huang, Kai

doi:10.1109/tii.2022.3141408

Cited by 53 publications

(16 citation statements)

References 75 publications

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“…The main part of this project stage was to figure out ways to pick up sounds in general. The challenge of identifying various frightening sound occurrences may be broken down into two separate subtasks [34]:…”

Section: Model Overviewmentioning

confidence: 99%

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Momynkulov¹,

Dosbayev²,

Suliman³

et al. 2023

Computers, Materials &Amp; Continua

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Video analytics is an integral part of surveillance cameras. Compared to video analytics, audio analytics offers several benefits, including less expensive equipment and upkeep expenses. Additionally, the volume of the audio datastream is substantially lower than the video camera datastream, especially concerning real-time operating systems, which makes it less demanding of the data channel's bandwidth needs. For instance, automatic live video streaming from the site of an explosion and gunshot to the police console using audio analytics technologies would be exceedingly helpful for urban surveillance. Technologies for audio analytics may also be used to analyze video recordings and identify occurrences. This research proposed a deep learning model based on the combination of convolutional neural network (CNN) and recurrent neural network (RNN) known as the CNN-RNN approach. The proposed model focused on automatically identifying pulse sounds that indicate critical situations in audio sources. The algorithm's accuracy ranged from 95% to 81% when classifying noises from incidents, including gunshots, explosions, shattered glass, sirens, cries, and dog barking. The proposed approach can be applied to provide security for citizens in open and closed locations, like stadiums, underground areas, shopping malls, and other places.

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Section: Model Overviewmentioning

confidence: 99%

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Momynkulov¹,

Dosbayev²,

Suliman³

et al. 2023

Computers, Materials &Amp; Continua

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show abstract

“…In the near future, vast number of things connected to the Internet will need an IPv6 address since the IPv4 address space will be effectively consumed [41]. Accordingly, IPv6 addressing is now desirable as it provides 2 128 unique addresses or approximately Presents a detailed review of the security-related challenges and sources of threat in the IoT applications Shapla Khanam et al [20] Presents an overview of IoT, its system architecture, enabling technologies, and discusses security challenges Bin Liao et al [21] Analyzes the security of IoT devices and provides the countermeasures in response to security problems and challenges by using mobile computing Salma et al [22] Presents the challenges of IoT security and the security requirements needed by IoT systems Vishal et al [23] Surveys the security, privacy and trust for Smart Mobile-Internet of Things Ismail et al [24] Presents a review of security attacks towards WSNs and IoT, along with the techniques for prevention, detection, and mitigation of those attacks Pawani et al [25] Surveys the security considerations with 6G enabling technologies Shalitha et al [26] Surveys the exploitation of Network Slicing for Internet of Things in 5G Networks Van-Linh Nguyen et al [27] Surveys the security and privacy issues based on prospective technologies for 6G Dinh C. Nguyen et al [28] Surveys wireless artificial intelligence applications in various data-driven domains Yasmine et al [29] Provides an up-to-date vision of the current research topics related to IoT security and propose a taxonomy of IoT attacks and analyze the security vulnerabilities of IoT at different layers Pasika et al [30] Analyse the security and privacy of the Multi-Access Edge Computing system Zhiyuan Yu et al [31] Surveys the security and privacy problems arising from the interaction of cyber world and physical world, with the context of broad cyber-physical applications Euijong Lee et al [32] Surveys the international standards related to interoperability and security for IoT environments Xing et al [33] Provides an overview of IoT equipment's physical security and safety, antitheft and antivandalism schemes along with circuit and system design, additional sensing devices, biometry and behavior analysis, and tracking methods Arup et al [34] Surveys the security and privacy threats for bluetooth low energy in IoT and wearable devices Lijun et al [35] Analyse various dimensions of trust, dynamic changes of context, privacy preserving, and cross-domain issues Jiani et al [36] Provides general security guidelines for IoT-enabled smart city developments, highlights common security challenges and reviews recent cryptographic security implementations for IoT-enabled smart cities Emilie et al…”

Section: Iot Ecosystemmentioning

confidence: 99%

A Novel Architectural Framework on IoT Ecosystem, Security Aspects and Mechanisms: A Comprehensive Survey

et al. 2022

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For the past few years, the Internet of Things (IoT) technology continues to not only gain popularity and importance, but also witnesses the true realization of everything being smart. With the advent of the concept of smart everything, IoT has emerged as an area of great potential and incredible growth. An IoT ecosystem centers around innovation perspective which is considered as its fundamental core. Accordingly, IoT enabling technologies such as hardware and software platforms as well as standards become the core of the IoT ecosystem. However, any large-scale technological integration such as the IoT development poses the challenge to ensure secure data transmission. Perhaps, the ubiquitous and the resource-constrained nature of IoT devices and the sensitive and private data being generated by IoT systems make them highly vulnerable to physical and cyber threats. In this paper, we re-define an IoT ecosystem from the core technologies view point. We propose a modified three layer IoT architecture by dividing the perception layer into elementary blocks based on their attributed functions. Enabling technologies, attacks and security countermeasures are classified under each layer of the proposed architecture. Additionally, to give the readers a broader perspective of the research area, we discuss the role of various state-of-the-art emerging technologies in the IoT security. We present the security aspects of the most prominent standards and other recently developed technologies for IoT which might have the potential to form the yet undefined IoT architecture. Among the technologies presented in this article, we give a special interest to one recent technology in IoT domain. This technology is named IQRF that stands for Intelligent Connectivity using Radio Frequency. It is an emerging technology for wireless packet-oriented communication that operates in sub-GHz ISM band (868 Mhz) and which is intended for general use where wireless connectivity is needed, either in a mesh network or pointto-point (P2P) configuration. We also highlighted the security aspects implemented in this technology and we compare it with the other already known technologies. Moreover, a detailed discussion on the possible attacks is presented. These attacks are projected on the IoT technologies presented in this article including IQRF. In addition, lightweight security solutions, implemented in these technologies, to counter these threats in the proposed IoT ecosystem architecture are also presented. Lastly, we summarize the survey by listing out some common challenges and the future research directions in this field.INDEX TERMS Cyber attacks, IoT architecture, Internet of Things (IoT) ecosystem, IQRF, Network security.

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“…As seen in the vulnerabilities section earlier, there are various weaknesses that can be potentially exploited when it comes to edge nodes/devices that are responsible for interacting with the physical environment and the system [57], [85]. The first step to prevent cyber-attacks in the perception layer is to design the IIoT systems physically secure.…”

Section: ) Edge Protectionmentioning

confidence: 99%

Cyber-Security of Industrial Internet of Things in Electric Power Systems

2022

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Electric Power Systems (EPSs) are among the most critical infrastructures of any society, since they significantly impact other infrastructures. Recently, there has been a trend toward implementing modern technologies, such as Industrial Internet of Things (IIoT), in EPSs to enhance their real-time monitoring, control, situational awareness, and intelligence. This movement, however, has exposed EPSs to various cyber intrusions that originate from the IIoT ecosystem. Statistics show that 38% of reported attacks have been against power and water infrastructure, and so far at least 91% of power utilities have experienced a cyber-attack. The cyber-security problem is even more severe for IIoT applications in EPSs due to the vulnerabilities and resource limitations of such applications. Thus, based on the above statistics, it is necessary to investigate the vulnerabilities of IIoT-based applications in EPSs, identify probable attacks and their consequences, and develop intrusion prevention and detection approaches to secure IIoT systems. On this basis, this paper first elaborates on the applications of IIoT-based systems in EPSs, and evaluates their security challenges. Afterwards, it comprehensively reviews various cyber-attacks against IIoT-assisted EPSs, with a particular focus on attack entry points and adversarial methods. Finally, efforts to prevent cyber-intrusions against IIoT systems in EPSs are explained, and different attack detection techniques are discussed.INDEX TERMS Cyber-attacks, cyber-security, electric power systems, industrial internet of things, intrusion detection systems. I. TRANSFORMATION OF IOT TO IIOT

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Physical Security and Safety of IoT Equipment: A Survey of Recent Advances and Opportunities

Cited by 53 publications

References 75 publications

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

A Novel Architectural Framework on IoT Ecosystem, Security Aspects and Mechanisms: A Comprehensive Survey

Cyber-Security of Industrial Internet of Things in Electric Power Systems

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