Hamming Distance Model Research Articles

Modeling of TST Using Biological Signals and KL-Hausdorff Distance Similarity Measure for Gene Based Parkinson’s Disease Recognition

Genetic research experienced drastic transformation since past decades, which benefits the biological area eventually for the detection of neurodegenerative ailment like Parkinson’s disease (PD). Recently, rigorous investigate had been conceded out for of PDs detection instigated through-sequence -and recessive auto-somal-of dominant-genes such as PARK2, LRRK2, SNCA, PARK7 and PINK1. Several inherent based similarity degree representations such as Cosine similarity and Hamming Distance model were introduced for the detection of these genes. However, these representations detect 2 to 3 gene sequence barely by maximum Root Mean Square Error (RMSE) and minimum accuracy rate. The ratio of misclassification is too great for prevailing scheme. To perceive PD through low RMSE and high accuracy a Kullback-Leibler Hausdroff distance (KL-H) similarity measure model is proposed so as to discover the affected patient pattern efficiently. It works in two phases, in first, protein sequence of amino acid is determined with the use of model transcription, splicing and translation (TST). The second stage in turn distinguish PD that depends on the model of similarity measure which comprise assessment of template sequence and specified sequence with the use of Hausdorff distance and KL-distance process. The property of nucleotide density in KL distance measure algorithm was employed. The result analysis and comparative study were presented among the proposed and existing system. We attained maximum accuracy of 88%, with sensitivity 67.86%, specificity 93.81%, precision 76%, F1 score 71.69%, minimum RMSE (12%) and FPR (6.19%)in comparison to the prevailing similarity measurement model.

Intuitionistic fuzzy hamming distance model for failure detection in a slewing gear system

Intuitionistic fuzzy hamming distance model for failure detection in a slewing gear system

Deep learning side-channel attack against hardware implementations of AES

In the field of image recognition, machine learning technologies, especially deep learning, have been rapidly advancing alongside the advances of hardware such as GPUs. In image recognition, in general, large numbers of labeled images to be identified are input to a neural network, and repeatedly learning the images enables the neural network to identify objects with high accuracy. A new profiling side-channel attack method, the deep learning side-channel attack (DL-SCA), utilizes the neural network’s high identifying ability to unveil a cryptographic module’s secret key from side-channel information. In DL-SCAs, the neural network is trained with power waveforms captured from a target cryptographic module, and the trained network extracts the leaky part that depends on the secret. However, at this stage, the main target of investigation has been software implementation, and studies regarding hardware implementation, such as ASIC, are somewhat lacking. In this paper, we first depict deep learning techniques, profiling side-channel attacks, and leak models to clarify the relation between secret and side channels. Next, we investigate the use of DL-SCA against hardware implementations of AES and discuss the problem derived from the Hamming distance model and ShiftRow operation of AES. To solve the problem, we propose a new network training method called “mixed model dataset based on round-round XORed value.” We prove that our proposal solves the problem and gives the attack capability to neural networks. We also compare the attack performance and characteristics of DL-SCA to conventional analysis methods such as correlation power analysis and conventional template attack. In our experiment, a dedicated ASIC chip for side-channel analysis is utilized and the chip is also equipped with a side-channel countermeasure AES. We show how DL-SCA can recover secret keys against the side-channel countermeasure circuit. Our results demonstrate that DL-SCA can be a more powerful option against side-channel countermeasure implementations than conventional SCAs.

A Simple, Fast, Filter-Based Algorithm for Approximate Circular Pattern Matching.

This paper deals with the approximate version of the circular pattern matching (ACPM) problem, which appears as an interesting problem in many biological contexts. The circular pattern matching problem consists in finding all occurrences of the rotations of a pattern P of length m in a text T of length n. In ACPM, we consider occurrences with k -mismatches under the Hamming distance model. In this paper, we present a simple and fast filter-based algorithm to solve the ACPM problem. We compare our algorithm with the state of the art algorithms and the results are found to be excellent. In particular, our algorithm runs almost twice as fast than the state of the art. Much of the efficiency of our algorithm can be attributed to its filters that are effective but extremely simple and lightweight.

A combinational power analysis method against cryptographic hardware

Power analysis is a non-invasive attack against cryptographic hardware, which effectively exploits runtime power consumption characteristics of circuits. This paper proposes a new power model which combines Hamming Distance model and the model based on the template value of power consumption in combinational logic circuit. The new model can describe the power consumption characteristics of sequential logic circuits and those of combinational logic as well. The new model can be used to improve the existing power analysis methods and detect the information leakage of power consumption. Experimental results show that, compared to CPA(Correlation Power Analysis) method, our proposed attack which adopt the combinational model is more efficient in terms of the number of required power traces.

Optimal Hamming Distance Model for Crypto Cores against Side Channel Threats

Optimal Hamming Distance Model for Crypto Cores against Side Channel Threats

Optimal Hamming Distance Model for Crypto Cores against Side Channel Threats

Microelectronic crypto devices contain intellectual property like secret data to be protected against side channel attack. Scan chain based attacks come under the category of side channel attack where the hackers attack a scan path through observing and comparing the relationship between intermediate hamming distances values for different test vector patterns. Hence our novel hamming model should overcome the scan based attack and should not give any correlation relationship in hamming distance by providing the similar intermediate values for all test vector patterns which are obtained through an optimal way of inserting Optimal Scan Flip Flop (OSFF) randomly to the scan path chain. Implementation of our proposed integrated circuits is written in Verilog and synthesised with XILINX Spartan III FPGA. The report is compared with Robust Scan Flip Flop (RFSS) hamming model to estimate the overhead of component minimized in OSFF.

Fast algorithms for approximate circular string matching

BackgroundCircular string matching is a problem which naturally arises in many biological contexts. It consists in finding all occurrences of the rotations of a pattern of length m in a text of length n. There exist optimal average-case algorithms for exact circular string matching. Approximate circular string matching is a rather undeveloped area.ResultsIn this article, we present a suboptimal average-case algorithm for exact circular string matching requiring time O(n). Based on our solution for the exact case, we present two fast average-case algorithms for approximate circular string matching with k-mismatches, under the Hamming distance model, requiring time O(n) for moderate values of k, that is k=O(m/logm). We show how the same results can be easily obtained under the edit distance model. The presented algorithms are also implemented as library functions. Experimental results demonstrate that the functions provided in this library accelerate the computations by more than three orders of magnitude compared to a naïve approach.ConclusionsWe present two fast average-case algorithms for approximate circular string matching with k-mismatches; and show that they also perform very well in practice. The importance of our contribution is underlined by the fact that the provided functions may be seamlessly integrated into any biological pipeline. The source code of the library is freely available at http://www.inf.kcl.ac.uk/research/projects/asmf/.

Extending alignments with k-mismatches and ℓ-gaps

Recently, the problem of extending an alignment with k-mismatches and a single gap for pairwise sequence alignment was introduced (Flouri et al., 2011). The authors considered the problem of extending an alignment under the Hamming distance model by also allowing the insertion of a single gap; and presented a Θ(mβ)-time algorithm to solve it, where m is the length of the shortest sequence to be extended, and β is the maximum allowed length of the single gap. Very recently, it was shown (Flouri et al., 2012) that this problem is strongly and directly motivated by the next-generation re-sequencing application: aligning tens of millions of short DNA sequences against a reference genome. In this article, we consider an extension of this problem: extending an alignment with k-mismatches and two gaps; and present a Θ(mβ)-time algorithm to solve it. This extension is proved to be fundamental in the next-generation re-sequencing application (Alachiotis et al., 2012). In addition, we present a generalisation of our solution to solve the problem of extending an alignment with k-mismatches and ℓ-gaps in time Θ(mβℓ). The presented solutions work provided that all gaps in the alignment must occur in one of the two sequences.

A Comparative Study of Power Consumption Models for CPA Attack

Power analysis attacks are types of side channel attacks that are based on analyzing the power consumption of the cryptographic devices. Correlation power analysis is a powerful and efficient cryptanalytic technique. It exploits the linear relation between the predicted power consumption and the real power consumption of cryptographic devices in order to recover the correct key. The predicted power consumption is determined by using the appropriate consumption model. Until now, only a few models have been proposed and used. In this paper, we describe the process to conduct the CPA attack against AES on SASEBO-GII board. We present a comparison between the Hamming Distance model and the Switching Distance model, in terms of number of power traces needed to recover the correct key using these models. The global successful rate achieves 100% at 11100 power traces. The power traces needed to recover the correct key have been decreased by 12.6% using a CPA attack with Switching Distance model.

Hamming Distance Model Based Power Analysis for Cryptographic Algorithms

In order to evaluate the security of Application Specific Integrated Circuit (ASIC) implemented cryptographic algorithms at an early design stage, a Hamming distance model based power analysis is proposed. The Data Encryption Standard (DES) algorithm is taken as an example to illustrate the threats of differential power analysis (DPA) attack against the security of ASIC chip. A DPA attack against the ASIC implementation of a DES algorithm is realized based on hamming distance power model (HD model), and it realized the attack by successfully guessing the right 48-bit subkey. This result indicates that the power analysis attack based on the HD model is simple, rapid and effective for the design and evaluation of security chips.

Correlation power analysis of Trivium

ABSTRACTCorrelation power analysis (CPA) has been a powerful and thoroughly studied threat for implementations of block ciphers and public key algorithms but not yet for stream ciphers. This paper proposes a novel CPA attack on the hardware‐oriented stream cipher Trivium, one of the finally chosen ciphers by the eSTREAM project. Based on the Hamming distance model, the proposed attack exploits the resynchronization phase of Trivium. By choosing proper initial value vectors, the algorithmic noise of the device is completely eliminated. Furthermore, a novel concept of modified correlation coefficients is introduced, which can be used to describe the relation between the hypothetical power consumption values and the measured power consumption values. Through the calculation of modified correlation coefficients, the effect of the electronic noise is significantly decreased and values of the hypotheses can be discriminated uniquely by the highest modified correlation coefficient. According to the recovered hypotheses, many equations on the secret key bits can be obtained, which will be sequentially solved to extract the secret key of Trivium. Compared with Fischer's differential power analysis attack on Trivium, the proposed algorithm is more efficient and robust. Finally, a simulation attack is mounted to confirm the efficiency of the algorithm. Copyright © 2011 John Wiley & Sons, Ltd.

The Switching Glitch Power Leakage Model

Power analysis attacks are based on analyzing the power consumption of the cryptographic devices while they perform the encryption operation. Correlation Power Analysis (CPA) attacks exploit the linear relation between the known power consumption and the predicted power consumption of cryptographic devices to recover keys. It has been one of the effective side channel attacks that threaten the security of CMOS circuits. However, few works consider the leakage of glitches at the logic gates. In this paper, we present a new power consumption model, namely Switching Glitch (SG) power leakage model, which not only considers the data dependent switching activities but also including glitch power consumptions in CMOS circuits. Additionally, from a theoretical point of view, we show how to estimate the glitch factor. The experiments against AES implementation validate the proposed model. Compared with CPA based on Hamming Distance model, the power traces of recovering keys have been decreased by as much as 28.9%.