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Low Area PRESENT Cryptography in FPGA Using TRNG-PRNG Key Generation

Computers, Materials & Continua 2021年第8期68卷 1447-1465页

作者： T.Kowsalya R.Ganesh Babu B.D.Parameshachari Anand Nayyar Raja Majid Mehmood Department of Electronic and Communication Engineering Muthayammal Engineering CollegeRasipuramNamakkalTamilnaduIndia Department of Electronics and Communication Engineering SRM TRP Engineering CollegeTiruchirappalliTamilnaduIndia Department of Telecommunication Engineering GSSS Institute of Engineering and Technology for WomenMysuruIndia Faculty of Information Technology Graduate SchoolDuy Tan UniversityDa Nang550000Viet Nam Department of Information and Communication Technology School of Electrical and Computer EngineeringXiamen University MalaysiaSepang43900Malaysia

Lightweight Cryptography(LWC)is widely used to provide integrity,secrecy and authentication for the sensitive ***,the LWC is vulnerable to various constraints such as high-power consumption,time consumption,and hardware utilization and susceptible to the malicious *** order to overcome this,a lightweight block cipher namely PRESENT architecture is proposed to provide the security against malicious *** True random number generator-pseudo random number generator(TRNG-PRNG)based key generation is proposed to generate the unpredictable keys,being highly difficult to predict by the ***,the hardware utilization of PRESENT architecture is optimized using the Dual port Read Only Memory(DROM).The proposed PRESENT-TRNGPRNG architecture supports the 64-bit input with 80-bit of key *** performance of the PRESENT-TRNG-PRNG architecture is evaluated by means of number of slice registers,flip flops,number of slices Look Up Table(LUT),number of logical elements,slices,bonded input/output block(IOB),frequency,power and *** input retrieval performances analyzed in this PRESENT-TRNG-PRNG architecture are Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Mean-Square Error(MSE).The PRESENT-TRNG-PRNG architecture is compared with three different existing PRESENT architectures such as PRESENT On-TheFly(PERSENT-OTF),PRESENT Self-Test Structure(PRESENT-STS)and PRESENT-Round Keys(PRESENT-RK).The operating frequency of the PRESENT-TRNG-PRNG is 612.208 MHz for Virtex 5,which is high as compared to the PRESENT-RK.

关键词： Dual port read only memory hardware utilization lightweight cryptography malicious attackers present block cipher pseudo random number generator true random number generator

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A New Approach for the DFT NIST Test Applicable for Non-Stationary Input Sequences

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Journal of Signal and Information Processing 2021年第1期12卷 1-41页

作者： Yehonatan Avraham Monika Pinchas Department of Electrical and Electronic Engineering Ariel University Ariel Israel

The National Institute of Standards and Technology (NIST) document is a list of fifteen tests for estimating the probability of signal randomness degree. Test number six in the NIST document is the Discrete Fourier Transform (DFT) test suitable for stationary incoming sequences. But, for cases where the input sequence is not stationary, the DFT test provides inaccurate results. For these cases, test number seven and eight (the Non-overlapping Template Matching Test and the Overlapping Template Matching Test) of the NIST document were designed to classify those non-stationary sequences. But, even with test number seven and eight of the NIST document, the results are not always accurate. Thus, the NIST test does not give a proper answer for the non-stationary input sequence case. In this paper, we offer a new algorithm or test, which may replace the NIST tests number six, seven and eight. The proposed test is applicable also for non-stationary sequences and supplies more accurate results than the existing tests (NIST tests number six, seven and eight), for non-stationary sequences. The new proposed test is based on the Wigner function and on the Generalized Gaussian Distribution (GGD). In addition, this new proposed algorithm alarms and indicates on suspicious places of cyclic sections in the tested sequence. Thus, it gives us the option to repair or to remove the suspicious places of cyclic sections

关键词： Wigner Distribution Shape Parameter Generalized Gaussian Distribution random number generator True random number generator pseudo random number generator

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