Biometric Identification Based on Keystroke Dynamics
Paweł Kasprowski, Zaneta Borowska, Katarzyna Harȩżlak
Abstract
The purpose of the paper is to study how changes in neural network architecture and its hyperparameters affect the results of biometric identification based on keystroke dynamics. The publicly available dataset of keystrokes was used, and the models with different parameters were trained using this data. Various neural network layers-convolutional, recurrent, and dense-in different configurations were employed together with pooling and dropout layers. The results were compared with the state-of-the-art model using the same dataset. The results varied, with the best-achieved accuracy equal to 82% for the identification (1 of 20) task.
Topics & Concepts
Keystroke dynamicsBiometricsDropout (neural networks)Computer sciencePoolingIdentification (biology)Convolutional neural networkKeystroke loggingHyperparameterArtificial intelligenceMachine learningTask (project management)Artificial neural networkPattern recognition (psychology)Data miningSpeech recognitionEngineeringComputer securityPasswordS/KEYSystems engineeringBotanyBiologyUser Authentication and Security SystemsBiometric Identification and SecurityAdvanced Malware Detection Techniques