Authenticated key establishment for low-resource devices exploiting correlated random channels

Zenger, Christian; Pietersz, Mario; Zimmer, Jan; Posielek, Jan-Felix; Lenze, Thorben; Paar, Christof

doi:10.1016/j.comnet.2016.06.013

Cited by 37 publications

(23 citation statements)

References 34 publications

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“…The system does so by controlling the traffic flows of vulnerable devices, thus protecting other devices in the network from threats and preventing data leakage. Zenger et al [44] proposed a vicinity-based pairing mechanism that delegates trust from one node to another based on physical proximity. Messaging between devices can be authenticated using multiple communication channels (e.g.…”

Section: A Iot Device Identification and Authenticationmentioning

confidence: 99%

Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

Restuccia

D’Oro

Melodia

2018

IEEE Internet Things J.

224

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The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a "patch" against known attacks are insufficient. Indeed, nextgeneration IoT challenges will require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. To this end, machine learning and software-defined networking will be key to provide both reconfigurability and intelligence to the IoT devices. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges related to the application of machine learning and software-defined networking to address existing and nextgeneration IoT security threats.

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Section: A Iot Device Identification and Authenticationmentioning

confidence: 99%

Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

Restuccia

D’Oro

Melodia

2018

IEEE Internet Things J.

224

View full text Add to dashboard Cite

show abstract

“…In order to obtain correlated channel observations, key generation is usually studied with the aid of systems operating in time division duplexing (TDD) mode, e.g. Wi-Fi [6]- [9], ZigBee [4], [10]- [12], Bluetooth [13], and LoRa [14]- [16], etc. 2 A station is a device that supports Wi-Fi functions in the Wi-Fi terminology.…”

Section: Related Workmentioning

confidence: 99%

“…This technique generates keys between a pair of legitimate users with no assistance from a third party. Using a lightweight procedure, compared to the popular elliptic curve-based Diffie-Hellman (ECDH) PKC scheme, the results in [4] indicate that an ECDH protocol dissipates about 100 times more energy and imposes about 1000 times higher complexity than the key generation counterpart, when both are implemented by an 8bit Intel MCS-51 micro-controller. Key generation has also been proved to be information theoretically-secure, as it relies on the unpredictable random wireless channels [5].…”

Section: Introductionmentioning

confidence: 99%

“…In other scenarios, the controller may have to transmit unicast messages securely to each device, which will require different private session keys established between the controller and each device. Key generation has been mainly applied to pairs of users [4], [6]- [16]. Substantial research efforts have been invested in designing group key generation [17]- [21], but channel probing still has to be carried out in a pairwise manner, which imposes substantial cost in terms of the number of transmissions and channel occupation.…”

Section: Introductionmentioning

confidence: 99%

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Design of an Efficient OFDMA-Based Multi-User Key Generation Protocol

Zhang

Ding

Liu

et al. 2019

IEEE Trans. Veh. Technol.

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Secret key generation exploits the unique random features of wireless channels, hence it is eminently suitable for the resource constrained Internet of Things applications. However, it has only been involved for single links between a pair of users, whilst there is a paucity of literature on group and multi-user key generation. This paper proposes an orthogonal frequency-division multiple access (OFDMA)-based multi-user key generation protocol to efficiently establish keys in a star topology. The uplink and downlink multi-user access facilitated by OFDMA allows the central node to simultaneously communicate with multiple users, which can significantly reduce the channel probing overhead. In particular, we provide a compelling case study of multi-user secret key generation by designing a prototype based on IEEE 802.11ax, a new Wi-Fi standard to be released. Our simulation results have demonstrated that the OFDMA-based multi-user key generation protocol incurs low interference amongst the users, whilst benefiting from channel reciprocity and generating unique random keys.

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“…Finally, key generation is lightweight and uses limited resources as all of the operations are not complicated and thus meets the low computation capacity of IoT devices. Zenger et al implemented their key generation scheme in a 32-bit ARM Cortex M3 processor (EFM32GG-STK3700) and an 8-bit Intel MCS-51 and showed the resource and energy consumption to be very low [35]. The authors also implemented a lightweight PKC, elliptic curves Diffie-Hellman key generation (ECDH), as a comparison.…”

Section: Physical Layer Key Generationmentioning

confidence: 99%

Securing Wireless Communications of the Internet of Things from the Physical Layer, An Overview

Zhang

Duong

Woods

et al. 2017

Entropy

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Abstract:The security of the Internet of Things (IoT) is receiving considerable interest as the low power constraints and complexity features of many IoT devices are limiting the use of conventional cryptographic techniques. This article provides an overview of recent research efforts on alternative approaches for securing IoT wireless communications at the physical layer, specifically the key topics of key generation and physical layer encryption. These schemes can be implemented and are lightweight, and thus offer practical solutions for providing effective IoT wireless security. Future research to make IoT-based physical layer security more robust and pervasive is also covered.

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Authenticated key establishment for low-resource devices exploiting correlated random channels

Cited by 37 publications

References 34 publications

Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

Design of an Efficient OFDMA-Based Multi-User Key Generation Protocol

Securing Wireless Communications of the Internet of Things from the Physical Layer, An Overview

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