Neural Networks and the Quest for Identity

I’m a pretty fair typist.  I’d say my current speed is roughly 60 words per minute–I was more accurate before this keyboard made it so easy to delete. What I am not good at and the instance in which I do more deleting per character is inputting passwords.  Password requests make me tense up, and even for those passwords I remember perfectly well, I tend to let the characters ripple off my fingertips.  Often my efforts to establish my identity are rejected, and this means I have to return to the dialog box, slow down, and pick at the keys like a chicken going for the grit.

 
So you can understand why the latest innovation based on a neural network is bad news for me. A Boston company, Delfigo, has just released computer security system that uses a neural network to authenticate whether or not the user is who he or she is attempting to pass as.  
 
As you’re probably aware, neural networks are good at recognizing and classifying patterns.  The new identity authentication system attends to the electrical signals coming from the computer keyboard and attempts to match them to the known typing "signature" of anyone with legitimate access to a network or website.  The input data for the neural network consists of "dwell" time and "flight" time, how long a finger remains on a key and how much time that finger spends in suspended in the air above the key.  This does not bode good things for a person whose key striking is as inconsistent as mine.  My identity will be hard to authenticate, and the confidence score generated by the neural network will be a little shaky.
 
My identity in question–but how can this be?  Who I am is how I type?

Sure is, and a word to the wise: Protect your identity. Hunt and peck no more.

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