Extending limited datasets with GAN-like self-supervision for SMS spam detection

Short Message Service (SMS) spamming is a harmful phishing attack on mobile phones. That is, fraudsters are trying to misuse personal user information, using tricky text messages, sometimes included with a fake URL that asks for this personal information, such as passwords, usernames, etc. In the world of Machine Learning, several approaches have tried to attitudinize this problem, but the lack of available data resources was commonly the main drawback towards a good enough solution. Therefore, in this paper, we suggest a dataset extension technique for small datasets, based on an Out Of Distribution (OOD) metric. Hence, different approaches such as Generative Adversarial Networks (GANs) were suggested, yet GANs are hard to train whenever datasets are limited in terms of sample size. In this paper, we present a GAN-like method that imitates the generator concept of GANs for the purpose of limited datasets extension, using the OOD concept. By using a sophisticated text generation method, we show how to apply it over datasets from the domain of fraud and spam detection in SMS messages, and achieve over 25% relative improvement, compared to two other solutions. In addition, due to the class imbalance in typical spam datasets, our approach is being examined over another dataset, in order to verify that the false alarm rate is low enough.