An Adaptive and Collaborative Server-Side SMS Spam Filtering Scheme Using Artificial Immune System

Abstract

ABSTRACTSMS Spam, which is an unsolicited or unwanted message, is a major problem with Global System for Mobile Communication (GSM) subscribers. Existing Spam filters have not been able to stop the SMS Spam problem due to frequent drift in spammer’s words, limited bag of words for training, device portability, and high computational overhead of filters. This paper presents a collaborative and adaptive server-side SMS Spam filter using Artificial Immune System (coined ExAIS_SMS). The proposed scheme involves five modules: the innate mechanism, the user feedback, the quarantine, the tokenizer, and the immune engine. In this study, a new English corpus consisting of 5,240 SMS messages from 20 different users was collected for the study. A comprehensive experimental analysis on the SMS data set reveals the constant changes of Spam keywords and the impact of user feedback for system adaptability. In order to prove the efficiency of the proposed scheme, ExAIS_SMS was benchmarked with existing systems using the NUS corpus. The result gave an overall accuracy of 99% for ExAIS_SMS, 98% for Bayesian, and 97% for a client side AIS. The results showed that ExAIS_SMS is an efficient SMS Spam filtering technique, especially in resource constrained mobile phones.