Biblio

Short messages usage has been tremendously increased such as SMS, tweets and status updates. Due to its popularity and ease of use, many companies use it for advertisement purpose. Hackers also use SMS to defraud users and steal personal information. In this paper, the use of Graphs centrality metrics is proposed for spam SMS detection. The graph centrality measures: degree, closeness, and eccentricity are used for classification of SMS. Graphs for each class are created using labeled SMS and then unlabeled SMS is classified using the centrality scores of the token available in the unclassified SMS. Our results show that highest precision and recall is achieved by using degree centrality. Degree centrality achieved the highest precision i.e. 0.81 and recall i.e., 0.76 for spam messages.

With technological advancements and increment in content based advertisement, the use of Short Message Service (SMS) on phones has increased to such a significant level that devices are sometimes flooded with a number of spam SMS. These spam messages can lead to loss of private data as well. There are many content-based machine learning techniques which have proven to be effective in filtering spam emails. Modern day researchers have used some stylistic features of text messages to classify them to be ham or spam. SMS spam detection can be greatly influenced by the presence of known words, phrases, abbreviations and idioms. This paper aims to compare different classifying techniques on different datasets collected from previous research works, and evaluate them on the basis of their accuracies, precision, recall and CAP Curve. The comparison has been performed between traditional machine learning techniques and deep learning methods.