cluster analysis - a simple/practical example of fuzzy c-means algorithm -

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I am writing my master thesis on the topic of dynamic keystroke authentication. To support ongoing research, I am writing code to test different extraction of feature extraction and feature matching.

My current simple approach just checks that the password passcode keycodes match the currently typed keycodes and also checks the key bar (residual) and key bar (flight) reference times Are +/- 100ms (tolerance). It's definitely very limited and I want to extend it with fuzzy C-pattern pattern matching.

These features are visible for each key: Keycode, Resident, Flight Time (First flight time is always 0)

Obviously that kikod can be removed from the fuzzy algorithm because they should be exactly the same. In this context, what does the practical implementation of Fuzzy C-sense look like?

Normally, you do the following:

  1. How many Want to cluster (2? "Authentic" and "fake"?)
  2. Determine what element you want to cluster (personal keystroke? Login attempt?)
  3. Determine what your feature vector will look like (time, flight time?)
  4. Determine which distance metric you will use (which distance will you measure the copy Each sample from each cluster?)
  5. Examples of each cluster type model training (What does an authentic login look like?)
  6. Clusters to run the FCM algorithm on training data
  7. To create a membership vector for any login attempt sample, run it via FCM algorithm using the clusters found in step 6
  8. The resultant member Using test vector in (based on threshold criteria) the login attempt is authentic

I'm not an expert, but it certainly is that login attempt is authentic or not. I have seen that the FCM has been used for pattern recognition (i.e., what is the expression of the face?), Which makes sense, because with defining characteristics you can choose from many categories (like happy, sad, Anger, etc ...). In your case, actually you only have definitions of a category (authentic), non-authentic keystrokes are not just like "authentic keystrokes", so they will not cluster.

Maybe I'm missing something?


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