Content-based SMS spam filtering based on the Scaled Conjugate Gradient backpropagation algorithm

Content-based filtering is one of the most preferred methods to combat Short Message Service (SMS) spam. Memory usage and classification time are essential in SMS spam filtering, especially when working with limited resources. Therefore, suitable feature selection metric and proper filtering technique should be used. In this paper, we investigate how a learnt Artificial Neural Network with the Scaled Conjugate Gradient method (ANN-SCG) is suitable for content-based SMS spam filtering using a small size of features selected by Gini Index (GI) metric. The performance of ANN-SCG is evaluated in terms of true positive rate against false positive rate, Matthews Correlation Coefficient (MCC) and classification time. The evaluation results show the ability of ANN-SCG to filter SMS spam successfully with only one hundred features and a short classification time around to six microseconds. Thus, memory size and filtering time are reduced. An additional testing using unseen SMS messages is done to validate ANN-SCG with the one hundred features. The result again proves the efficiency of ANN-SCG with the one hundred features for SMS spam filtering with accuracy equal to 99.1%.