Normal discriminant deep convolution neural classification based web behavioral pattern mining for user identification (Retracted Article)

Mining the user behavior patterns from huge log files plays a major role in discovering the web user identity. Several data mining techniques describe the way of predicting the user identity using web access behavior patterns. Accurate prediction of the user identity is still a challenging issue. The proposed work introduces a normal discriminant Tanimoto similarity based deep convolution feedforward neural learning classification (NDTS-DCFNLC) technique to improve the user identifications with their web access behavior patterns. The NDTS-DCFNLC technique comprises multiple layers. Normal discriminant preprocessing is carried out in layer one to remove the unwanted patterns from the web access log files. Similarity among the relevant web pattern is calculated using Tanimoto similarity at layer two. Sigmoid activation function is used in output layer to classify the frequently accessed patterns with higher accuracy and minimum error based on similarity threshold. Experimental testing is done using apache weblog with different number of patterns and the diverse constraints such as classification accuracy, false positive rate, space requirements and execution time. Results were compared with linear temporal logic and KNN classification methods. The outcome shows NDTS-DCFNLC technique increases classification accuracy, decreases the false positives, execution time and the space requirements.