  In this paper, we introduce a new gait segmentation method based on
accelerometer data and develop a new distance function between two time series,
showing novel and effectiveness in simultaneously identifying user and
adversary. Comparing with the normally used Neural Network methods, our
approaches use geometric features to extract walking cycles more precisely and
employ a new similarity metric to conduct user-adversary identification. This
new technology for simultaneously identify user and adversary contributes to
cybersecurity beyond user-only identification. In particular, the new
technology is being applied to cell phone recorded walking data and performs an
accuracy of $98.79\%$ for 6 classes classification (user-adversary
identification) and $99.06\%$ for binary classification (user only
identification). In addition to walking signal, our approach works on walking
up, walking down and mixed walking signals. This technology is feasible for
both large and small data set, overcoming the current challenges facing to
Neural Networks such as tuning large number of hyper-parameters for large data
sets and lacking of training data for small data sets. In addition, the new
distance function developed here can be applied in any signal analysis.
