Developing new continuous learning approach for spam detection using artificial neural network (CLA-ANN)

There are several approaches which try to stop or reduce the huge amount of spam on individuals. These approaches include legislative measures such as anti-spam laws over world-wide. Other techniques are known as Origin-Based filters which are based on using network information and IP addresses in order to detect whether a message is spam or not. The most common techniques are the filtering techniques attempting to identify whether a message is spam or not based on the content and other characteristics of the message. In this paper, we apply core odifications on ANN in the input layers which allow the input layers to be changed over time and to replace useless layers with new promising layers which give us promising results. We call our approach Continuous Learning Approach Artificial Neural Network CLA-ANN. This work is evaluated using SpamAssassin Corpus which is rarely used with ANN.