RePID-OK: Spam Detection Using Repetitive Pre-processing

Email proves to be a convenient and powerful communication tool but it has given rise to unwanted mails. Spam mails leads to wastage of server storage space, consumption of network bandwidth and heavy financial losses to the organization, thus a serious research issue. Filtering mails is one of the popular approaches used to block spam mails. In this work, we propose RePID-OK (Repetitive Preprocessing technique using Imbalanced Data set by selecting Optimal number of Keywords) model for spam detection. Using the data set Ling-Spam, we show that efficiency of the proposed model is more powerful and effective than existing schemes. The performance of the proposed RePID-OK has been checked across the identified parameters and also evaluated against other existing models, thus demonstrating the efficiency of the proposed technique over other models in this area of research.