Predicting Spam Messages Using Back Propagation Neural Network

With the increase in popularity of smartphones, text-based communication has also gained popularity. Availability of messaging services at low cost has resulted into the increase in spam messages. This increase in number of spam messages has become an important issue these days. Many mobile applications are developed to detect spam messages in mobile phones but still, there is a lack of a complete solution. This paper presents an approach for the detection of spam messages. We have identified an effective feature set for text messages which classify the messages into spam or ham with high accuracy. The feature selection procedure is implemented on normalized text messages to obtain a feature vector for each message. The feature vector obtained is tested on a set of machine learning algorithms to observe their efficiency. This paper also presents a comparative analysis of different algorithms on which the features are implemented. In addition, it presents the contribution of different features in spam detection. After implementation and as per the set of features selected, Artificial Neural Network Algorithm using Back Propagation technique works in the most efficient manner.