Dataset for Towards Liveness Detection in Keystroke Dynamics: Revealing Synthetic Forgeries

Dataset used in the article "The Reverse Problem of Keystroke Dynamics: Guessing Typed Text with Keystroke Timings". CSV files with dataset results summaries, the evaluated sentences, detailed results, and scores. Results data contains training and evaluation ARFF files for each user, containing features of synthetic and legitimate samples as described in the article. The source data comes from three free text keystroke dynamics datasets used in previous studies, by the authors (LSIA) and two other unrelated groups (KM, and PROSODY, subdivided in GAY, GUN, and REVIEW). Two different languages are represented, Spanish in LSIA and English in KM and PROSODY. The original dataset KM was used to compare anomaly-detection algorithms for keystroke dynamics in the article "Comparing anomaly-detection algorithms forkeystroke dynamic" by Killourhy, K.S. and Maxion, R.A. The original dataset PROSODY was used to find cues of deceptive intent by analyzing variations in typing patterns in the article "Keystroke patterns as prosody in digital writings: A case study with deceptive reviews and essay" by Banerjee, R., Feng, S., Kang, J.S., and Choi, Y. We introduce two strategies using higher order contexts and empirical distributions to generate artificial samples of keystroke timings, together with a liveness detection system for keystroke dynamics that leverages them as adversaries. To aid with this objective, a new derived feature based on the inverse function of the smoothed empirical cumulative distributions is presented. One of the proposed attacking strategies outperforms other methods previously evaluated in the literature by a large margin, doubling and sometimes tripling their false acceptance rates, to around 15%, when data of the targeted user is available. If only general population data is available to an attacker, the liveness detection system achieves false acceptance and false rejection rates between 1% and 2%, consistently, over three datasets.