Negative Selection with High-Dimensional Support for Keystroke Dynamics

Computing and communication systems have been expanding and bringing a number of advancements to our way of life. However, this technological evolution has also contributed to the rise of the identity theft, mainly due to the advent of the digital identity. An alternative to overcome this problem is by the analysis of the user behavior, known as behavioral intrusion detection. Among the possible aspects to be analysed, this work focuses on the keystroke dynamics, which consists of recognizing users by their typing rhythm. This paper draws a comparison between some novelty detectors applied to keystroke dynamics: immune negative selection algorithms and auto-associative neural networks. Issues regarding the use of negative selection in high dimensional spaces are discussed and an alternative to deal with this problem is presented.