Lightweight cryptography in IoT networks: A survey

Rana, Muhammad; Mamun, Quazi; Islam, Rafiqul

doi:10.1016/j.future.2021.11.011

Cited by 100 publications

(48 citation statements)

References 82 publications

(101 reference statements)

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“…Many surveys on research progress in IoT security have been published in recent years [18], [26]- [28]. Researchers have mainly focused on IoT security solutions.…”

Section: Related Workmentioning

confidence: 99%

“…The seven metrics are the block/key size, memory, gate area, latency, throughput, power & energy, and hardware & software efficiency. In a recent study, Rana et al [28] discussed state-of-the-art lightweight cryptographic protocols for IoT networks and provided a comparative analysis of popular ciphers. The authors discussed three lightweight cryptography primitives: the block cipher, stream cipher, and elliptic curve cipher.…”

Section: Related Workmentioning

confidence: 99%

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Lightweight Cryptographic Hash Functions: Design Trends, Comparative Study, and Future Directions

et al. 2022

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The emergence of the Internet of Things (IoT) has enabled billions of devices that collect large amounts of data to be connected. Therefore, IoT security has fundamental requirements. One critical aspect of IoT security is data integrity. Cryptographic hash functions are cryptographic primitives that provide data integrity services. However, due to the limitations of IoT devices, existing cryptographic hash functions are not suitable for all IoT environments. As a result, researchers have proposed various lightweight cryptographic hash function algorithms. In this paper, we discuss advanced lightweight cryptographic hash functions for highly constrained devices, categorize design trends, analyze cryptographic aspects and cryptanalytic attacks, and present a comparative analysis of different hardware and software implementations. In the final section of this paper, we highlight present research challenges and suggest future research topics related to the design of lightweight cryptographic hash functions.

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“…Many surveys on research progress in IoT security have been published in recent years [18], [26]- [28]. Researchers have mainly focused on IoT security solutions.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Lightweight Cryptographic Hash Functions: Design Trends, Comparative Study, and Future Directions

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Furthermore, the use of the traditional methods for providing security, which is known by the Triangle and defined by Confidentiality, Integration, and Authentication (CIA) [11], needs to be reconsidered. The process of encryption of data between two communication devices (two nodes) and associated operations, such as key exchange and encryption, are also considered traditional [12]. Moreover, these technologies are energy-intensive methods, especially as we mentioned in the previous paragraph, in the constant change in network topologies due to the constant movement of WSN nodes.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Ahmad

Wazirali

Abu-Ain

2022

Sensors

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Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.

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“…The Internet of Things (IoT) is a contemporary technology that links a large number of items to the internet, including wearable devices, sensors, smartphones, smart meters, and auto-mobiles [ 1 , 2 ] It offers services and cost-effective solutions in a variety of fields, including healthcare, smart grid, industrial manufacturing, smart cities, business, and smart railway infrastructure [ 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Compact Finite Field Multiplication Processor Structure for Cryptographic Algorithms in IoT Devices with Limited Resources

Ibrahim

Gebali

2022

Sensors

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The rapid evolution of Internet of Things (IoT) applications, such as e-health and the smart ecosystem, has resulted in the emergence of numerous security flaws. Therefore, security protocols must be implemented among IoT network nodes to resist the majority of the emerging threats. As a result, IoT devices must adopt cryptographic algorithms such as public-key encryption and decryption. The cryptographic algorithms are computationally more complicated to be efficiently implemented on IoT devices due to their limited computing resources. The core operation of most cryptographic algorithms is the finite field multiplication operation, and concise implementation of this operation will have a significant impact on the cryptographic algorithm’s entire implementation. As a result, this paper mainly concentrates on developing a compact and efficient word-based serial-in/serial-out finite field multiplier suitable for usage in IoT devices with limited resources. The proposed multiplier structure is simple to implement in VLSI technology due to its modularity and regularity. The suggested structure is derived from a formal and systematic technique for mapping regular iterative algorithms onto processor arrays. The proposed methodology allows for control of the processor array workload and the workload of each processing element. Managing processor word size allows for control of system latency, area, and consumed energy. The ASIC experimental results indicate that the proposed processor structure reduces area and energy consumption by factors reaching up to 97.7% and 99.2%, respectively.

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Lightweight cryptography in IoT networks: A survey

Cited by 100 publications

References 82 publications

Lightweight Cryptographic Hash Functions: Design Trends, Comparative Study, and Future Directions

Lightweight Cryptographic Hash Functions: Design Trends, Comparative Study, and Future Directions

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Compact Finite Field Multiplication Processor Structure for Cryptographic Algorithms in IoT Devices with Limited Resources

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