An extended analytical framework for heterogeneous implementations of light cryptographic algorithms

Damaj, Issam; Al-Mubasher, Hadi; Saadeh, Mahmoud

doi:10.1016/j.future.2022.11.007

Cited by 6 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Developing aggregated performance indices that accurately assess trade-offs for each implementation is highly desirable. This approach, aligning with their relative importance, would significantly enhance the research reported in this paper [33][34][35].…”

Section: Plos Onementioning

confidence: 85%

Optimizing FPGA implementation of high-precision chaotic systems for improved performance

Damaj,

Zaher,

Lawand

2024

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Developing chaotic systems-on-a-chip is gaining much attention due to its great potential in securing communication, encrypting data, generating random numbers, and more. The digital implementation of chaotic systems strives to achieve high performance in terms of time, speed, complexity, and precision. In this paper, the focus is on developing high-speed Field Programmable Gate Array (FPGA) cores for chaotic systems, exemplified by the Lorenz system. The developed cores correspond to numerical integration techniques that can extend to the equations of the sixth order and at high precision. The investigation comprises a thorough analysis and evaluation of the developed cores according to the algorithm complexity and the achieved precision, hardware area, throughput, power consumption, and maximum operational frequency. Validations are done through simulations and careful comparisons with outstanding closely related work from the recent literature. The results affirm the successful creation of highly efficient sixth-order Lorenz discretizations, achieving a high throughput of 3.39 Gbps with a precision of 16 bits. Additionally, an outstanding throughput of 21.17 Gbps was achieved for the first-order implementation coupled with a high precision of 64 bits. These outcomes set our work as a benchmark for high-performance characteristics, surpassing similar investigations reported in the literature.

show abstract

Section: Plos Onementioning

confidence: 85%

Optimizing FPGA implementation of high-precision chaotic systems for improved performance

Damaj,

Zaher,

Lawand

2024

PLoS ONE

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, complex networks themselves often evolve dynamically over time, and new links are often connected between some nodes. Fortunately, the technique of link prediction in complex networks aims to discover hidden or future links between network nodes, including the prediction of unobserved links, i.e., links that actually exist in a network but have not yet been detected, and the prediction of future links , i.e., links that do not exist in the network at present but should exist or are likely to exist in the future [3][4][5][6][7][8][9] . Link prediction, serving as an abstraction for numerous widespread issues, can be utilized in any system that transforms entities and their relationships into a network representation.…”

Section: Incorporating High-frequency Information Into Edge Convoluti...mentioning

confidence: 99%

Incorporating high-frequency information into edge convolution for link prediction in complex networks

Zhang,

Xu,

Zhu

2024

Sci Rep

View full text Add to dashboard Cite

Link prediction in complex networks aims to mine hidden or to-be-generated links between network nodes, which plays a significant role in fields such as the cold start of recommendation systems, knowledge graph completion and biomedical experiments. The existing link prediction models based on graph neural networks, such as graph convolution neural networks, often only learn the low-frequency information reflecting the common characteristics of nodes while ignoring the high-frequency information reflecting the differences between nodes when learning node representation, which makes the corresponding link prediction models show over smoothness and poor performance. Focusing on links in complex networks, this paper proposes an edge convolutional graph neural network EdgeConvHiF that fuses high-frequency node information to achieve the representation learning of links so that link prediction can be realized by implementing the classification of links. EdgeConvHiF can also be employed as a baseline, and extensive experiments on real-world benchmarks validate that EdgeConvHiF not only has high stability but also has more advantages than the existing representative baselines.

show abstract

“…However, in [ 26 ], the authors Damaj et al proposed a unified analytical framework for evaluating lightweight cryptographic algorithms in heterogeneous computing environments, considering both hardware and software metrics. The framework suggested in the study utilizes three decision-making approaches TOPSIS, PROMETHEE II, and Fuzzy TOPSIS, to enable effective evaluations and adjustments to cryptographic algorithm implementations.…”

Section: Literature Surveymentioning

confidence: 99%

Efficiency and Security Evaluation of Lightweight Cryptographic Algorithms for Resource-Constrained IoT Devices

Radhakrishnan,

Jadon,

Honnavalli

2024

Sensors

View full text Add to dashboard Cite

The IoT has become an integral part of the technological ecosystem that we all depend on. The increase in the number of IoT devices has also brought with it security concerns. Lightweight cryptography (LWC) has evolved to be a promising solution to improve the privacy and confidentiality aspect of IoT devices. The challenge is to choose the right algorithm from a plethora of choices. This work aims to compare three different LWC algorithms: AES-128, SPECK, and ASCON. The comparison is made by measuring various criteria such as execution time, memory utilization, latency, throughput, and security robustness of the algorithms in IoT boards with constrained computational capabilities and power. These metrics are crucial to determine the suitability and help in making informed decisions on choosing the right cryptographic algorithms to strike a balance between security and performance. Through the evaluation it is observed that SPECK exhibits better performance in resource-constrained IoT devices.

show abstract

An extended analytical framework for heterogeneous implementations of light cryptographic algorithms

Cited by 6 publications

References 20 publications

Optimizing FPGA implementation of high-precision chaotic systems for improved performance

Optimizing FPGA implementation of high-precision chaotic systems for improved performance

Incorporating high-frequency information into edge convolution for link prediction in complex networks

Efficiency and Security Evaluation of Lightweight Cryptographic Algorithms for Resource-Constrained IoT Devices

Contact Info

Product

Resources

About