Frequency spectrograms for biometric keystroke authentication using neural network based classifier

Keystroke recognition is one of the branch of biometrics that is designed to strengthen regular passwords through inter-key times to protect the password owner from fraud attacks. The signals of keystrokes are usually evaluated only in the time domain since the applied systems collect and analyze only the time values. In addition to these kinds of algorithms, we introduce the extraction of novel frequency feature and a keystroke authentication system which has a classifier operating in frequency domain. The frequency extraction is a new approach that will enhance the authentication protocols and shed light on the keystroke authentication by providing a hidden security level. Above all, instead of inter-key times, the exact key press times are extracted and binarized in time domain. Subsequently, the spectrograms are generated by regular short time Fourier transform with the optimized window size. Since the spectrograms include both frequency and time data, represented as images, low frequencies under a threshold are erased and the high frequencies are collected in bins after the digitization. Consequently the average bin values are used as the inputs to train the Gauss-Newton based Neural Network classifier to validate the attempts. The results are highly promising that we obtained 4.1% Equal Error Rate (EER) after 60 real attempts of the password owner and 60 fraud attacks from 12 different users. The outcomes of this research enhance our understanding of knowledge-based classifiers for authentication as well as the Gauss-Newton based optimization for vectorial inputs of spectrogram analysis.