LAUSR

Email Spam is a variety of automated spam where unbidden messages, used for business purpose, sent extensively to multiple mailing lists, individuals or newsgroups. To build a fruitful system for spam detection, we introduced Random Forest integrated with Deep Neural network to find the classification accuracy. The Random Forest algorithm uses a preordained probability of attributes in constructing their decision trees. The Gini measure is examined to rank the important features. The main objective is to grade the features using RF algorithm and to train the data using Deep Neural Network Classifier. Deep Neural Network Classifier model (DNNs) are trained using backpropagation algorithm in batch learning mode, which requires the entire training data to learn at once. The detector process was dynamically fit to the new data patterns till it reaches the spam coverage. Experimental results shows that classification rate of DNN is higher than compared to KNN and Support Vector Machine(SVM) with an accuracy of 88.59% while considering the top ranked five features.