Non-email spam and machine learning-based anti-spam filters: trends and some remarks

Abstract

Electronic spam, or unsolicited and undesired messages sent massively, is one of the threats that affects email and other media. The high volume and ratio of email spam have generated enormous time and economic losses. Due to this, many different email anti-spam defenses have been used. This translated into more complex spams in order to surpass them. Moreover, the spamming business moved to the less protected yet quite profitable non-email media because of the numerous potential targets that results from their extensive usage. Since that moment, spams in these media have increased rapidly in quantity, sophistication and danger, especially in the most popular ones: Instant Messaging, SMS and social media. Therefore, in this paper some of the characteristics and statistics of instant spam, mobile spam and social spam are exposed. Then, an overview of anti-spam techniques developed during the last decade to fight these new spam trends is presented, focusing on hybrid and Machine Learning-based approaches. We conclude with some possible future evolutionary steps of both non-email spams and anti-spams.