A Machine Learning Approach to Contact Databases’ Importation for Spam Prevention

Abstract

This paper aims to provide a solution to a problem shared by online marketing platforms. Many of these platforms are exploited by spammers to ease their job of distributing spam. This can lead to platforms domains being black-listed by ISP’s, which translates to lower deliverability rates and consequently lower profits. Normally, platforms try to counter the problem by using rule-based systems, which require high-maintenance and are not easily editable. Additionally, since analysis occurs when a contact database is imported, the regular approach of judging messages’ contents directly is not an effective solution, as those do not yet exist. The proposed solution, a machine-learning based system for the classification of contact database’s importations, tries to surpass these aforementioned systems by making use of the capabilities introduced by machine-learning technologies, namely, reliability in regards to classification and ease of maintenance. Preliminary results show the legitimacy of this approach, since various algorithms can be successfully applied to it. The most proficient of the ones applied being Ada-boost and Random-forest.