Dynamic Time Warping Sample Clauses

Dynamic Time Warping. DTW is a well-known algorithm to measure similarity between two temporal sequences and find the most similar points between them. In other words, this technique is able to quantify the similarity between two signals (even if they are not completely aligned) and obtain the optimal match (alignment). In Figs. 4 and 5 the difference between the “traditional” euclidean distance and the warped dis- tance is demonstrated. Originally, DTW was used in speech recognition [30] but later on, it has been proved its applicability in several fields like gesture recog- nition, robotics, manufacturing, etc. This technique has been applied also in the SCA field with the elastic alignment [35] as a special kind of alignment using DTW (FastDTW [31], more precisely). This kind of alignment was proposed in order to address cryptographic implementations with random delay coun- termeasures. Afterwards, ▇▇▇▇▇▇▇▇ et al. proposed another alignment algorithm which can deal with this countermeasure [26]. This method can align traces with less computational effort than elastic alignment (DTW algorithm is relatively computationally costly, depending on the length of the path). However, we now propose the usage of DTW algorithm for an entirely different task: assessing differences between tracesets or devices. Our approach is to use DTW to quan- tify how similar two devices are by measuring the similarity of two temporal sequences representing the leakage of each device.
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