• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.683-688
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• 2011

The wireless sensor network (WSN) is well known for an enabling technology for the ubiquitous environment such as real-time surveillance system, habitat monitoring, home automation and healthcare applications. However, the WSN featuring wireless communication through air, a resource constraints device and irregular network topology, is threatened by malicious nodes such as eavesdropping, forgery, illegal modification or denial of services. For this reason, security in the WSN is key factor for utilizing the sensor network into the commercial way. There is a series of symmetric cryptography proposed by laboratory or industry for a long time. Among of them, recently proposed HUMMINGBIRD algorithm, motivated by the design of the well-known Enigma machine, is much more suitable to resource constrained devices, including smart card, sensor node and RFID tags in terms of computational complexity and block size. It also provides resistance to the most common attacks such as linear and differential cryptanalysis. In this paper, we implements ultra-lightweight cryptography, HUMMINGBIRD algorithm into the resource constrained device, sensor node as a perfectly customized design of sensor node.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6B
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• pp.414-422
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• 2012

In the paper we present optimized implementation method over sensor mote for HUMMINGBIRD2 algorithm, ultra-light symmetric cryptography. For efficient implementation we maximized the register usage and used optimized addressing method to reduce the encryption and decryption time. With the optimized encryption implementation, we can utilize the efficient secure network over resource constrained sensor mote.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.8
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• pp.1946-1963
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• 2012

Hummingbird is a lightweight encryption and message authentication primitive published in RISC'09 and WLC'10. In FSE'11, Markku-Juhani O.Saarinen presented a differential divide-and-conquer method which has complexity upper bounded by $2^{64}$ operations and requires processing of few megabytes of chosen messages under two related nonces (IVs). The improved version, Hummingbird-2, was presented in RFIDSec 2011. Based on the idea of differential collision, this paper discovers some weaknesses of the round function WD16. Combining with the simple key loading algorithm, a related-key chosen-IV attack which can recover the full secret key is proposed. Under 15 pairs of related keys, the 128 bit initial key can be recovered, requiring $2^{27}$ chosen IV and the computational complexity is $O(2^{27})$. In average, the attack needs several minutes to recover the full 128-bit secret key on a PC. The experimental result corroborates our attack. The result shows that the Hummingbird-2 cipher can't resist related key attack.

• Steel and Composite Structures
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• v.50 no.4
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• pp.443-458
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• 2024

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO₂ emissions in the building industry. In the present work, the compressive strength (f_c) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R² metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.