Realizing an Internet of Secure Things: A Survey on Issues and Enabling Technologies

Hamad, Salma Abdalla; Sheng, Quan Z.; Zhang, Wei Emma; Nepal, Surya

doi:10.1109/comst.2020.2976075

Cited by 85 publications

(31 citation statements)

References 137 publications

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“…Several solutions have been proposed [146]- [149]. A central aspect is to restrict the centralized processing of neural information and ensure privacy.…”

Section: Security Privacy and Ethical Aspectsmentioning

confidence: 99%

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

Moioli

Nardelli

Barros

et al. 2021

IEEE Commun. Surv. Tutorials

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This paper presents the first comprehensive tutorial on a promising research field located at the frontier of two wellestablished domains, neurosciences and wireless communications, motivated by the ongoing efforts to define the Sixth Generation of Mobile Networks (6G). In particular, this tutorial first provides a novel integrative approach that bridges the gap between these two seemingly disparate fields. Then, we present the state-ofthe-art and key challenges of these two topics. In particular, we propose a novel systematization that divides the contributions into two groups, one focused on what neurosciences will offer to future wireless technologies in terms of new applications and systems architecture (Neurosciences for Wireless Networks), and the other on how wireless communication theory and nextgeneration wireless systems can provide new ways to study the brain (Wireless Networks for Neurosciences). For the first group, we explain concretely how current scientific understanding of the brain would enable new applications within the context of a new type of service that we dub brain-type communications and that has more stringent requirements than human-and machine-type communication. In this regard, we expose the key requirements of brain-type communication services and discuss how future wireless networks can be equipped to deal with such services. Meanwhile, for the second group, we thoroughly explore modern communication systems paradigms, including Internet of Bio-Nano Things and wireless-integrated brainmachine interfaces, in addition to highlighting how complex systems tools can help bridging the upcoming advances of wireless technologies and applications of neurosciences. Brain-controlled vehicles are then presented as our case study to demonstrate for both groups the potential created by the convergence of neurosciences and wireless communications, probably in 6G. In summary, this tutorial is expected to provide a largely missing articulation between neurosciences and wireless communications while delineating concrete ways to move forward in such an interdisciplinary endeavor.

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“…Several solutions have been proposed [146]- [149]. A central aspect is to restrict the centralized processing of neural information and ensure privacy.…”

Section: Security Privacy and Ethical Aspectsmentioning

confidence: 99%

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

Moioli

Nardelli

Barros

et al. 2021

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…This will help adversaries gain control of the IoT devices. Such potential reasons may result in a series of different types of attacks in IoT, such as Distributed Denial of Services (DDoS), Man In The Middle (MITM), Zero-day, IoT botnets and ransomware [59]- [62]. Network-level security solutions are vital requisites to mitigate these attacks.…”

Section: Improving Securitymentioning

confidence: 99%

Survey on Network Slicing for Internet of Things Realization in 5G Networks

Wijethilaka

Liyanage

2021

IEEE Commun. Surv. Tutorials

310

124

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Internet of Things (IoT) is an emerging technology that makes people's lives smart by conquering a plethora of diverse application and service areas. In near future, the fifthgeneration (5G) wireless networks provide the connectivity for this IoT ecosystem. It has been carefully designed to facilitate the exponential growth in the IoT field. Network slicing is one of the key technologies in the 5G architecture that has the ability to divide the physical network into multiple logical networks (i.e. slices) with different network characteristics. Therefore, network slicing is also a key enabler of realisation of IoT in 5G. Network slicing can satisfy the various networking demands by heterogeneous IoT applications via dedicated slices. In this survey, we present a comprehensive analysis of the exploitation of network slicing in IoT realisation. We discuss network slicing utilisation in different IoT application scenarios, along with the technical challenges that can be solved via network slicing. Furthermore, integration challenges and open research problems related to the network slicing in the IoT realisation are also discussed in this paper. Finally, we discuss the role of other emerging technologies and concepts, such as blockchain and Artificial Intelligence/Machine Learning(AI/ML) in network slicing and IoT integration.

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“…Genetic algorithms have limitation for dealing with a complex feature set and machine learning algorithms used for classification increases the processing time and complexity of the system in the context of resource constraint devices. Privacy is an again big challenge especially when the cloud services are used for the classification of device identification [82]. Network fingerprinting does not work below the network layers in the TCP/IP stack.…”

Section: ) DI and Supervised Mlmentioning

confidence: 99%

Role of Device Identification and Manufacturer Usage Description in IoT Security: A Survey

et al. 2021

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This paper presents an overview of device identification techniques and the Manufacturer Usage Description (MUD) standard used for the Internet of things to reduce the IoT attack surface. The ongoing diversity and the sheer increase in the number of connected IoT devices have crumpled security efforts. There is a need to reconsider and redesign the underlying concept of developing security systems to resolve IoT security challenges. In this backdrop, device profiling and identification have emerged as an exciting technique that helps to reduce IoT device attack surface. One of the known approaches for device identification is to fingerprint a device. There are many ways to fingerprint the device, mostly using device network flows or device local attributes. The device identification ensures the authenticity of the device attached to the network, like user authentication. Since IoT devices mostly work using machine-to-machine (M2M) communication, this requires identifying each device properly. But there is no unified approach for device identification for the ever-growing world of IoT devices and applications. One of the major steps forward in this direction is the development of the Manufacturer Usage Description (MUD) standard that defines the role of a device within the network. It limits the device to execute the primary task only, which will help to reduce the attack surface. Since the inception of MUD, many security frameworks use this standard for IoT security. However, there is a need to scrutinize the security frameworks based on the MUD, to find out the claimed effectiveness of the standard in IoT security. This paper initially identifies and classifies the potential vulnerabilities in IoT devices. Then, the study provides an overview of the research that focuses on device identification techniques and analyzes their role in IoT security. Finally, the research presents an overview of MUD technology, its implementation scenarios, the limitation of the latest MUD standard, and its applications in the industry. The prime aim of this work is to examine the MUD benefits in IoT security along with the weaknesses and challenges while implementing this standard along with future directions. INDEX TERMSManufacturer usage description (MUD), Internet of Things (IoT), device identification (DI), software defined network (SDN), machine learning (ML), deep learning (DL). NOMAN MAZHAR received the B.E. degree in software engineering and the M.S. degree in information technology from the

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Realizing an Internet of Secure Things: A Survey on Issues and Enabling Technologies

Cited by 85 publications

References 137 publications

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

Survey on Network Slicing for Internet of Things Realization in 5G Networks

Role of Device Identification and Manufacturer Usage Description in IoT Security: A Survey

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