As the use of mobile phones grows, spams are becoming increasingly common in mobile communication such as SMS, calling for research on SMS spam detection. Existing detection techniques for SMS spams have been mostly adapted from those developed for other contexts such as emails and the web without taking into account some unique characteristics of SMS. Additionally, spamming tactics is constant...

In this paper, we aim to explore the possibility of Transformer model in detecting spam Short Message Service (SMS) messages by proposing a modified that is designed for SMS messages. The evaluation our proposed performed on Spam Collection v.1 dataset and UtkMl's Twitter Detection Competition dataset, with benchmark multiple established machine learning classifiers state-of-the-art detection a...

The efficient and accurate control of spams on mobile handsets is an important problem. Mobile spam incurs a cost on a per-message basis, degrades normal cellular service, and is a nuisance and breach of privacy. It is also a popular enabler of mobile fraud. In countries such as South Korea and Japan, Mobile Spamming generates almost half of the total SMS traffic. In this paper we propose a nov...

SMS or Short Message Service is usually found on cell phones. divided into two categories, namely spam and non-spam (ham). Spam an that annoying to phone users because it tends contain messages are not important such as promos scams. Meanwhile, (ham) tend SMS, from previous users. In this study, the classification of was carried out using logistic regression method. The purpose study distinguis...

In the modern life, SMS (Short Message Service) is one of the most necessary services on mobile devices. Because of its popularity, many companies use SMS as an effective marketing and advertising tool. Also, the popularity gives hackers chances to abuse SMS to cheat mobile users and steal personal information in their mobile phones, for example. In this paper, we propose a method to detect spa...

Text analysis includes lexical analysis of the text and has been widely studied and used in diverse applications. In the last decade, researchers have proposed many efficient solutions to analyze / classify large text dataset, however, analysis / classification of short text is still a challenge because 1) the data is very sparse 2) It contains noise words and 3) It is difficult to understand t...