On Emerging Family of Elliptic Curves to Secure Internet of Things: ECC Comes of Age

Liu, Zhe; Huang, Xinyi; Hu, Zhi; Khan, Muhammad Khurram; Seo, Hwajeong; Zhou, Lülin

doi:10.1109/tdsc.2016.2577022

Cited by 74 publications

(48 citation statements)

References 31 publications

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“…The linear relation between the time consumption and the input of the proposed schemes proves that DNA mapping before encryption and DNA remapping after decryption do not require complex quadratic computations. The energy consumption of the proposed scheme is slightly higher when compared to some of the existing designs owing to DNA mapping and remapping in the proposed scheme. However, despite an increase in energy consumption, the proposed scheme significantly increases the resilience of the elliptical cryptosystem.…”

Section: Simulation and Implementation Resultsmentioning

confidence: 91%

“… A: Input data length; B: Normalized input data length; C: Total execution time (DNA remapping + Decryption); D: CPU clock cycles; E: Core current; F: Core energy; G: Energy consumption reported in existing work . …”

Section: Simulation and Implementation Resultsmentioning

confidence: 99%

“…In the proposed DNA elliptical cryptography, standard elliptical curve parameters and methods have been chosen. Existing works in elliptical cryptography have shown its resilience towards timing and simple power analysis (SPA) attacks . The elliptical cryptography produces random data after decryption without a proper authentication key.…”

Section: Security Performancementioning

confidence: 99%

“…Therefore, encryption schemes for IoT systems need to be less complicated, and should function with reduced memory usage, which is dependent on the key size. Compared to conventional systems, elliptical curve cryptography (ECC) can statistically provide stronger security with a smaller key size, thus making it a suitable alternative for IoT devices . The strength of elliptical cryptography relies on the fact that the output of the decryption operation will be ambiguous without a proper authentication key.…”

Section: Introductionmentioning

confidence: 99%

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Novel Method for DNA-Based Elliptic Curve Cryptography for IoT Devices

Tiwari¹,

Kim²

2018

ETRI Journal

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Elliptic curve cryptography (ECC) can achieve relatively good security with a smaller key length, making it suitable for Internet of Things (IoT) devices. DNA-based encryption has also been proven to have good security. To develop a more secure and stable cryptography technique, we propose a new hybrid DNA-encoded ECC scheme that provides multilevel security. The DNA sequence is selected, and using a sorting algorithm, a unique set of nucleotide groups is assigned. These are directly converted to binary sequence and then encrypted using the ECC; thus giving double-fold security. Using several examples, this paper shows how this complete method can be realized on IoT devices. To verify the performance, we implement the complete system on the embedded platform of a Raspberry Pi 3 board, and utilize an active sensor data input to calculate the time and energy required for different data vector sizes. Connectivity and resilience analysis prove that DNAmapped ECC can provide better security compared to ECC alone. The proposed method shows good potential for upcoming IoT technologies that require a smaller but effective security system.

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Section: Simulation and Implementation Resultsmentioning

confidence: 91%

Section: Simulation and Implementation Resultsmentioning

confidence: 99%

Section: Security Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Novel Method for DNA-Based Elliptic Curve Cryptography for IoT Devices

Tiwari¹,

Kim²

2018

ETRI Journal

View full text Add to dashboard Cite

show abstract

“…However, it is infeasible to apply cryptography-based techniques in IoT due to the constraints of the energy and computational capability of smart objects [238], [239]. Therefore, new lightweight protection schemes are expected to be developed for IoT [240]. • Privacy and security in data storage.…”

Section: Security and Privacy Concernsmentioning

confidence: 99%

Big Data Analytics for Large-scale Wireless Networks

et al. 2019

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The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large scale wireless networks. Big data of large scale wireless networks has the key features of wide variety, high volume, real-time velocity and huge value leading to the unique research challenges that are different from existing computing systems. In this paper, we present a survey of the state-of-art big data analytics (BDA) approaches for large scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.

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Edge enhanced deep learning system for IoT edge device security analytics

Mukhtar

Mehrabi

Kong

et al. 2021

Concurrency and Computation

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The processing of locally harvested data at the physically accessible edge devices opens a new avenue of security threats for edge enhanced analytics. Cryptographic algorithms are used to secure the data being processed on the edge device. However, the implementation weakness of the algorithms on the edge devices can lead to side‐channel attack vulnerability, which is exacerbated with the application of machine‐learning techniques. This research proposes a deep learning‐based system integrated at the edge device to identify the side‐channel leakages. To design such a deep learning‐based system, one of the challenges is formulating the suitable attack model for the underlying target algorithm. Based on the previous findings, three machine learning‐based side‐channel attack models are curated and investigated for the edge device security evaluations. As a test case, the standard elliptic‐curve cryptographic algorithm is selected. Moreover, quantitative analysis is provided for the best attack model selection using standard machine‐learning evaluation metrics. A comparative analysis is performed on the raw unaligned data samples and reduced feature‐engineered samples using edge enhanced security analytics. The investigation concludes that the vulnerable algorithm implementation can lead to the secret key recovery from the edge device, with 96% accuracy, using a neural‐network‐based algorithm to analyse side‐channel attacks.

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On Emerging Family of Elliptic Curves to Secure Internet of Things: ECC Comes of Age

Cited by 74 publications

References 31 publications

Novel Method for DNA-Based Elliptic Curve Cryptography for IoT Devices

Novel Method for DNA-Based Elliptic Curve Cryptography for IoT Devices

Big Data Analytics for Large-scale Wireless Networks

Edge enhanced deep learning system for IoT edge device security analytics

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