SMS spamming is an activity of sending �unwanted messages� through text messaging or other communication services; normally using mobile phones. Nowadays there are many methods for SMS spam detection, ranging from the list-based, statistical algorithm, IP-based and using machine learning. However, an optimum method for SMS spam detection is difficult to find due to issues of SMS length, battery and memory performances. Hoping to minimize the aforementioned problems, this paper introduces another detection variance that is based on common characters used when sending SMS (i.e. numbers and symbols), SMS length and keywords. To verify our work, the proposed features were stipulated into five different algorithms and then, tested with three different datasets for their ability to detect spam. From the conduct of experiments, it can be suggested that these three features are reasonable to be used for detecting SMS spam as it produced positive results. In the future, it is anticipated that the proposed algorithm will perform better when combined with machine learning techniques. � Springer International Publishing Switzerland 2016.

This paper reported and summarized findings of spam management for Short Message Service (SMS) which consists of classification and clustering of spam using two different tools, namely RapidMiner and Weka. By using the same dataset, which is downloaded from UCI, Machine Learning Repository, various algorithms used in classification and clustering in this simulation has been analysed comparatively. From the simulation, both tools giving the similar results that the same classifiers are the best for SMS spam classification and clustering which are outperformed than other algorithms. . Keywords- SMS spam; RapidMiner; Weka; Naïve Bayesian (NB); Support Vector Machine (SVM); k-Nearest Neighbour (kNN); K-Mean; Cobweb; Hierarchical clustering; spam classification; spam clustering. .

Spamming activities using text messages on mobile phone are widely spreading, as in line with the development of technology for mobile phones. This phenomenon has contributed a major threat that impacts the usability of messages. Even though many techniques have been proposed and introduced for detecting these ‘unwanted’ messages, all those efforts still cannot bring this problem to an end. The major challenges in detecting and filtering spam messages today are ineffective solution to deal with strains of spam messages because of the variety content of messages and the attitude of users themselves. This paper aims to view the performance of Artificial Immune System (AIS) algorithms inspired from the ideology of Biology Immune System (BIS) in human body for detecting spam messages on mobile phone. Two types of AIS algorithms were used; Danger Theory (DT) and Negative Selection (NS). Their performances were measured and compared in terms of effectiveness, efficiency and Receiver Over Characteristic (ROC) area, tested on WEKA using three different datasets. From our conduction of experiments, generic Negative Selection algorithm performs bette