Аннотация
Evaluating classifier performance is a critical
problem in pattern recognition and machine learning. In this
paper pattern recognition techniques were applied to identify
encryption algorithms. Four different block cipher algorithms
were considered, DES, IDEA, AES, and RC2 operating in
(Electronic Codebook) ECB mode. Eight different classification
techniques were used for this purpose, these are: Naïve
Bayesian (NB), Support Vector Machine (SVM), neural
network (MLP), Instance based learning (IBL), Bagging (Ba),
AdaBoostM1 (MdaBM1), Rotation Forest (RoFo), and Decision
Tree (C4. 5). The result shows that using pattern recognition
is a useful technique to identify the encryption algorithm,
and according to our simulation using one encryption of key
provide better classification than using different keys.
Furthermore, increase the number of the input files will
improve the accuracy.
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