scholarly journals Optimal Hamming Distance Model for Crypto Cores against Side Channel Threats

2014 ◽  
Vol 7 (is4) ◽  
pp. 28-33
Author(s):  
K. P. Sridhar
Keyword(s):  
Hamming Distance ◽  
Side Channel ◽  
Distance Model

Hamming Distance Model Based Power Analysis for Cryptographic Algorithms

2011 ◽  
Vol 121-126 ◽  
pp. 867-871 ◽  
Author(s):  
Jie Li ◽  
Wei Wei Shan ◽  
Chao Xuan Tian
Keyword(s):  
Integrated Circuit ◽  
Power Analysis ◽  
Hamming Distance ◽  
Data Encryption ◽  
Design Stage ◽  
Early Design Stage ◽  
Model Based ◽  
Cryptographic Algorithms ◽  
Distance Model ◽  
The Right

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.

Leakage squeezing: Optimal implementation and security evaluation

2014 ◽  
Vol 8 (3) ◽  
Author(s):  
Claude Carlet ◽  
Jean-Luc Danger ◽  
Sylvain Guilley ◽  
Houssem Maghrebi
Keyword(s):  
Power Analysis ◽  
Hamming Distance ◽  
Security Evaluation ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Correlation Power Analysis ◽  
Zero Offset ◽  
Sophisticated Analysis ◽  
Sensitive Variable ◽  
Optimal Implementation

AbstractHardware devices can be protected against side-channel attacks by introducing one random mask per sensitive variable. The computation throughout is unaltered if the shares (masked variable and mask) are processed concomitantly, in two distinct registers. Nonetheless, this setup can still be attacked if the side-channel is squared, because this operation causes an interference between the two shares. This more sophisticated analysis is referred to as a zero-offset second-order correlation power analysis (CPA) attack. When the device leaks in Hamming distance, the countermeasure can be improved by the “leakage squeezing”. It consists in manipulating the mask through a bijection, aimed at reducing the dependency between the shares' leakage. Thus

scholarly journals Evaluating Hamming Distance as a Metric for the Detection of CRC-based Side-channel Communications in MANETs

2016 ◽  
Vol 83 ◽  
pp. 433-440
Author(s):  
Brent Moore ◽  
Miguel Vargas Martin ◽  
Ramiro Liscano
Keyword(s):  
Hamming Distance ◽  
Side Channel

AN OVERVIEW OF DISTANCE AND SIMILARITY MEASURES OF INTUITIONISTIC FUZZY SETS

2008 ◽  
Vol 16 (04) ◽  
pp. 529-555 ◽  
Author(s):  
Z. S. XU ◽  
J. CHEN
Keyword(s):  
Fuzzy Sets ◽  
Hausdorff Distance ◽  
Euclidean Distance ◽  
Hamming Distance ◽  
Similarity Measures ◽  
Intuitionistic Fuzzy Sets ◽  
Comprehensive Overview ◽  
Intuitionistic Fuzzy ◽  
Distance Model ◽  
Short Time

The Intuitionistic Fuzzy Sets (IFSs), originated by Atanassov [1], is a useful tool to deal with vagueness and ambiguity. In the short time since their first appearance, many different distance and similarity measures of IFSs have been proposed, but they are scattered through the literature. In this paper, we give a comprehensive overview of distance and similarity measures of IFSs. Based on the weighted Hamming distance, the weighted Euclidean distance, and the weighted Hausdorff distance, respectively, we define some continuous distance and similarity measures for IFSs. We also utilize geometric distance model to define some continuous distance and similarity measures for IFSs, which are the various combinations and generalizations of the weighted Hamming distance, the weighted Euclidean distance and the weighted Hausdorff distance. Then we extend these distance and similarity measures for Interval-Valued Intuitionistic Fuzzy Sets (IVIFSs).

scholarly journals The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations

2018 ◽  
pp. 209-237 ◽  
Author(s):  
Stjepan Picek ◽  
Annelie Heuser ◽  
Alan Jovic ◽  
Shivam Bhasin ◽  
Francesco Regazzoni
Keyword(s):  
Machine Learning ◽  
Hamming Distance ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Machine Learning Techniques ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Learning Techniques ◽  
Channel Analysis ◽  
Learning Metrics

We concentrate on machine learning techniques used for profiled sidechannel analysis in the presence of imbalanced data. Such scenarios are realistic and often occurring, for instance in the Hamming weight or Hamming distance leakage models. In order to deal with the imbalanced data, we use various balancing techniques and we show that most of them help in mounting successful attacks when the data is highly imbalanced. Especially, the results with the SMOTE technique are encouraging, since we observe some scenarios where it reduces the number of necessary measurements more than 8 times. Next, we provide extensive results on comparison of machine learning and side-channel metrics, where we show that machine learning metrics (and especially accuracy as the most often used one) can be extremely deceptive. This finding opens a need to revisit the previous works and their results in order to properly assess the performance of machine learning in side-channel analysis.

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

2021 ◽  
Vol 11 (10) ◽  
pp. 2566-2572
Author(s):  
B. Anusha ◽  
P. Geetha
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Similarity Measure ◽  
Hausdorff Distance ◽  
Distance Measure ◽  
Hamming Distance ◽  
Genetic Research ◽  
Measurement Model ◽  
Distance Model ◽  
Result Analysis

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.

scholarly journals Evaluating Hamming Distance as a CRC-based Side-channel Detection Measure in Wi-Fi Networks

2016 ◽  
Vol 83 ◽  
pp. 425-432 ◽  
Author(s):  
Visal Chea ◽  
Miguel Vargas Martin ◽  
Ramiro Liscano
Keyword(s):  
Hamming Distance ◽  
Side Channel ◽  
Channel Detection
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