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Low Area PRESENT Cryptography in FPGA Using TRNG-PRNG Key Generation
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作者 T.Kowsalya R.Ganesh Babu +2 位作者 B.D.Parameshachari Anand Nayyar Raja Majid Mehmood 《Computers, Materials & Continua》 SCIE EI 2021年第8期1447-1465,共19页
Lightweight Cryptography(LWC)is widely used to provide integrity,secrecy and authentication for the sensitive applications.However,the LWC is vulnerable to various constraints such as high-power consumption,time consu... Lightweight Cryptography(LWC)is widely used to provide integrity,secrecy and authentication for the sensitive applications.However,the LWC is vulnerable to various constraints such as high-power consumption,time consumption,and hardware utilization and susceptible to the malicious attackers.In order to overcome this,a lightweight block cipher namely PRESENT architecture is proposed to provide the security against malicious attacks.The 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 hackers.Moreover,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 value.The 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 delay.The 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. 展开更多
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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作者 Yehonatan Avraham Monika Pinchas 《Journal of Signal and Information Processing》 2021年第1期1-41,共41页
The National Institute of Standards and Technology (NIST) document is a list of fifteen tests for estimating the probability of signal randomness degree. <span style="font-family:Verdana;">Test number ... The National Institute of Standards and Technology (NIST) document is a list of fifteen tests for estimating the probability of signal randomness degree. <span style="font-family:Verdana;">Test number six in the NIST document is the Discrete Fourier Transform</span><span style="font-family:Verdana;"> (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 </span><span style="font-family:Verdana;">or test, which may replace the NIST tests number six, seven and eight. The</span> <span style="font-family:Verdana;">proposed test is applicable also for non-stationary sequences and supplies</span><span style="font-family:Verdana;"> more </span><span style="font-family:Verdana;">accurate results than the existing tests (NIST tests number six, seven and</span><span style="font-family:Verdana;"> eight), for non-stationary sequences. The new proposed test is based on the Wigner function and on the Generalized Gaussian Distribution (GGD). In addition, </span><span style="font-family:Verdana;">this new proposed algorithm alarms and indicates on suspicious places of</span><span style="font-family:Verdana;"> cyclic </span><span style="font-family:Verdana;">sections in the tested sequence. Thus, it gives us the option to repair or to</span><span style="font-family:Verdana;"> remove the suspicious places of cyclic sections</span><span><span><span><span></span><span></span><b><span style="font-family:;" "=""><span></span><span></span> </span></b></span></span></span><span><span><span><span></span><span></span><span style="font-family:;" "=""><span></span><span></span><span style="font-family:Verdana;">(this part is beyond the scope </span><span style="font-family:Verdana;">of this paper), so that after that, the repaired or the shortened sequence</span><span style="font-family:Verdana;"> (origi</span><span style="font-family:Verdana;">nal sequence with removed sections) will result as a sequence with high</span><span style="font-family:Verdana;"> probability of random degree.</span></span></span></span></span> 展开更多
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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