Short Message Service Spam Filtering Research Articles

Analysis and implementation of cross lingual short message service spam filtering using graph-based k-nearest neighbor

SMS (Short Message Service) is on e of the communication services that still be the main choice, although now the phone grow with various applications. Along with the development of various other communication media, some countries lowered SMS rates to keep the interest of mobile users. It resulted in increased spam SMS that used by several parties, one of them for advertisement. Given the kind of multi-lingual documents in a message SMS, the Web, and others, necessary for effective multilingual or cross-lingual processing techniques is becoming increasingly important. The steps that performed in this research is data / messages first preprocessing then represented into a graph model. Then calculated using GKNN method. From this research we get the maximum accuracy is 98.86 with training data in Indonesian language and testing data in indonesian language with K 10 and threshold 0.001.

A novel feature extraction approach in SMS spam filtering for mobile communication: one‐dimensional ternary patterns

AbstractThe importance and utilization of mobile communication are increasing day by day, and the short message service (SMS) is one of them. Although SMS is a widely used communication way, it brings together a major problem, which is SMS spam messages. SMS spams do not only use vain in the mobile communication traffic but also disturb users. Based on this fact, blacklisting methods, statistical methods which are built on the frequency of occurrence of words or characters, and machine learning methods have been employed. Because punishments and legal laws are not enough to solve this problem and the Group Special Mobile number of SMS spam can easily be changed, a content‐based approach must be proposed. Content‐based methods showed high success in spam e‐mail filtering, but it is hard in the SMS spam filtering because SMS messages are extremely short and generally contains many abbreviations. In this study, an image processing method, local ternary pattern was improved to extract features from SMS messages in the feature extraction stage. In the proposed one‐dimensional ternary patterns, firstly, text message was converted to their UTF‐8 values. Later, each character (its UTF‐8 value) in the message was compared with its neighbors. Two different feature sets were extracted from the results of these comparisons. Finally, some machine learning methods were employed to classify these features. In order to validate the proposed approach, three different SMS corpora were used. The achieved accuracies and other employee performance measures showed that the proposed approach, one‐dimensional ternary patterns, can be effectively employed in SMS spam filtering. Copyright © 2016 John Wiley & Sons, Ltd.

A Framework for SMS Spam and Phishing Detection in Malay Language: a Case Study

Short Message Service (SMS) spam and SMS phishing has been increase nowadays especially in Malay language which is the first language for Malaysia country. Currently, many SMS spam in others language has been proposed, however not yet for Malay language and we are the first to propose these. In addition, this paper also analyst on several frameworks of SMS spam filtering for our SMS spam and phishing detection framework. From the analysis, the chosen framework has been enhanced for Malay SMS spam and phishing. The enhancement has been done on classification phase where our framework proposed dual classification. The classification 1 will classify the SMS into ham and scam SMS. For classification 2, the scam SMS will be classified again into SMS spam and SMS phishing. After dual classifications phase completed, the Malay SMS has been examined using Naive Bayes and J48 unsupervised Machine Learning techniques. The result shows high accuracy in detecting Malay SMS ham, spam and phishing.

The Impact of Feature Extraction and Selection on SMS Spam Filtering

This paper investigates the impact of several feature extraction and feature selection approaches on filtering of short message service (SMS) spam messages in two different languages, namely Turkish and English. The entire feature set of filtering framework consists of the features originated from the bag-of-words (BoW) model along with the ensemble of structural features (SF) specific to spam problem. The distinctive BoW features are identified using information theoretic feature selection methods. Various combinations of the BoW and SF are then fed into widely used pattern classification algorithms to classify SMS messages. The filtering framework is evaluated on both Turkish and English SMS message datasets. For this purpose, as part of the study, the first publicly available Turkish SMS message collection is constituted as well. Comprehensive experimental analysis on the respective datasets revealed that the combinations of BoW and SFs, rather than BoW features alone, provide better classification performance on both datasets. Effectiveness of the utilized feature selection methods however slightly differs in each language. DOI: http://dx.doi.org/10.5755/j01.eee.19.5.1829