SHAKE: SHared Acceleration Key Establishment for Resource-Constrained IoT Devices

Bejder, Emil; Mathiasen, Adam Krog; Donno, Michele De; Dragoni, Nicola; Fafoutis, Xenofon

doi:10.1109/wf-iot48130.2020.9221263

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…IoT devices with limited processing power can use lightweight cryptography to encrypt their communications. Comparisons of NIST-defined LWC properties (cost, performance, and security) and discussions of different gaps and open research issues [127]. The literature study indicates that NIST has accredited PRESENT and CLEFIA block ciphers for cost-and securityrelated considerations.…”

Section: Discussionmentioning

confidence: 99%

A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

Kumar,

Dangi

et al. 2024

CMC

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The widespread and growing interest in the Internet of Things (IoT) may be attributed to its usefulness in many different fields. Physical settings are probed for data, which is then transferred via linked networks. There are several hurdles to overcome when putting IoT into practice, from managing server infrastructure to coordinating the use of tiny sensors. When it comes to deploying IoT, everyone agrees that security is the biggest issue. This is due to the fact that a large number of IoT devices exist in the physical world and that many of them have constrained resources such as electricity, memory, processing power, and square footage. This research intends to analyse resource-constrained IoT devices, including RFID tags, sensors, and smart cards, and the issues involved with protecting them in such restricted circumstances. Using lightweight cryptography, the information sent between these gadgets may be secured. In order to provide a holistic picture, this research evaluates and contrasts well-known algorithms based on their implementation cost, hardware/software efficiency, and attack resistance features. We also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio. KEYWORDSIoT; a sensor device; lightweight; cryptography; block cipher; smart card; security and privacy Intelligent Power Grids: The transfer of data and control instructions between smart metres, grid equipment, and the centralised grid management system in smart grid systems is encrypted using lightweight cryptography. These low-power cryptographic systems protect data authenticity and privacy with little re-source drain. Inventory Control:To ensure the safety of data sent between GPS-enabled tracking devices and the main monitoring platform, asset tracking systems utilise lightweight encryption. In this way, pre-cious assets may be tracked in real-time, and their whereabouts can be kept secret from prying eyes.Linked Automobiles: Communications between the many Internet of Things (IoT) components in a car, such as the infotainment system, telematics, and sensors, are encrypted using lightweight cryptography in connected vehicle systems. These lightweight cryptographic methods help keep vehicle data secure and private without slowing down response times.Intelligent Farming: Soil moisture sensors, drones, and controlled irrigation systems are only a few ex-amples of the Internet of Things devices that benefit from lightweight cryptography in precision agriculture. These gadgets provide information on the state of crops and their surrounding environments, while also saving electricity and protecting the privacy of crucial agricultural records.Environmental Monitoring: Lightweight cryptography is employed in Internet of Things sensors for environmental monitoring applications in uninhabited areas like woods and seas. Even in lowpower settings, these sensors may gather information about the weather, air quality, or animals, and send it on to a central monitoring system. Management of Retai...

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Section: Discussionmentioning

confidence: 99%

A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

Kumar,

Dangi

et al. 2024

CMC

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“…• Resource-constrained -Most IoT devices, such as smart watches, CCTV cameras, and Bluetooth-operated devices, are resource constrained and heterogeneous, making it easier for attackers to target the system [27].…”

Section: This Paper Fl For Iot Malware Analysismentioning

confidence: 99%

IoT Malware Analysis Using Federated Learning: A Comprehensive Survey

2023

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The Internet of Things (IoT) has paved the way to a highly connected society where all things are interconnected and exchanging information has become more accessible through the internet. With the use of IoT devices, the threat of malware has increased rapidly. The increased number of existing and new malware variants has made protecting IoT devices and networks challenging. The malware can hide in the systems and disables its activity when there are attempts to discover and detect them. With technological advances, there are various emerging techniques to address this problem. However, they still encounter issues concerning the privacy and security of the user's data and suffer from a single point of failure. To address this issue, there are recent research developments conducted to use Federated Learning (FL). FL is a decentralized technique that trains the user's data on-device and exchanges the parameters without sharing the user's data. FL is implemented to secure the user's data, provide safe and accurate models, and prevent the single point of failure in the centralized models. This paper provides an overview of different approaches that integrate FL with IoT. Finally, we discuss the applications of FL, the research challenges, and future research directions.

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“…Key amplification helps to increase the final key's randomness to eliminate the information leakage and increase entropy. Typically, two methods are used to combine keys generated from different segments and eliminate the correlation between them, bitwise XOR function [115]- [117] and hash function MD5 and SHA-256 [100], [101], [107], [108], [114], [131].…”

Section: E Key Amplificationmentioning

confidence: 99%

A Survey of Wearable Devices Pairing Based on Biometric Signals

Pourbemany¹,

Zhu²,

Bettati³

2021

Preprint

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With the growth of wearable devices, which are usually constrained in computational power and user interface, this pairing has to be autonomous. Considering devices that do not have prior information about each other, a secure communication should be established by generating a shared secret key derived from a common context between the devices. Context-based pairing solutions increase the usability of wearable device pairing by eliminating any human involvement in the pairing process. This is possible by utilizing onboard sensors (with the same sensing modalities) to capture a common physical context (e.g., body motion, gait, heartbeat, respiration, and EMG signal). A wide range of approaches has been proposed to address autonomous pairing in wearable devices. This paper surveys context-based pairing in wearable devices by focusing on the signals and sensors exploited. We review the steps needed for generating a common key and provide a survey of existing techniques utilized in each step.

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SHAKE: SHared Acceleration Key Establishment for Resource-Constrained IoT Devices

Cited by 9 publications

References 31 publications

A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

IoT Malware Analysis Using Federated Learning: A Comprehensive Survey

A Survey of Wearable Devices Pairing Based on Biometric Signals

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