Wearables such as smartwatches and fitness trackers are known for using their sensor data to recognize and classify sporting activities. However, research has shown several times that it is also possible to extract sensitive information such as Personal Identification Numbers (PINs) and passwords from this data. To protect users against this type of attack, this thesis presents the PIN Entering Detector (PINED), a security system that can recognize when a PIN is being entered and alert users of the potential danger. In contrast to previous approaches for similar protection frameworks, our solution does not explicitly exclude machine learning technologies. Instead, we use the advances that have taken place in the computation of such models. Therefore, it can use complex features for the classification and hereby achieve higher balanced classification accuracies. Subsequently, we first create a data set consisting of over 4500 data sequences of motion data to train two state-of-the-art machine learning models from the field of time series classification with it. After that we integrate the resulting binary classifiers into a smartwatch security app. The results show that with the data collected, both models were successfully trained to achieve a balanced accuracy of 85-90%, beating previous security systems. However, the classification results produced by the smartwatch app were too unreliable. Nevertheless, PINED showed the feasibility of a security framework based on machine learning.