Analysing keystroke dynamics using wavelet transforms

Many smartphones are lost every year, with a meager percentage recovered. In many cases, users with malicious intent access these phones and use them to acquire sensitive data. There is a need for continuous monitoring and surveillance in smartphones, and keystroke dynamics play an essential role in identifying whether a phone is being used by its owner or an impersonator. Also, there is a growing need to replace expensive 2-tier authentication methods like One-time passwords (OTP) with cheaper and more robust methods. The methods proposed in this paper are applied to existing data and are proven to train more robust classifiers. A novel feature extraction method by wavelet transformation is demonstrated to convert keystroke data into features. The comparative study of classifiers trained on the extracted features vs. features extracted by existing methods shows that the processes proposed perform better than the state-of-art feature extraction methods.