Classifying Arbitrary Bit Streams Using Shannon Entropy,
Statistical Distributions and
Euclidean Distance Vector Fitting Technique
15 September 1998
Edward G. Rice, Consultant
Research Associates of Syracuse Inc.
Presents the 2 man-month feasibility analysis obtained from evaluating 14
statistical parameters of 13,762 arbitrary bit stream samples. The results
demonstrate the feasibility of accurately discriminating and classifying arbitrary
bit streams with the goodness of fit values generated by a Euclidean distance
algorithm.
