SOCIAL HONEYPOTS ON INSTAGRAM: A STUDY ON TECHNOLOGIES AND METHODOLOGIES TO AUTOMATE THEM.

Online Social Networks (OSNs) have gained increasing popularity in recent years leading to very fast growth in terms of registered users. While OSNs are widely used for legitimate content sharing, their rapid growth has also led to the emergence of illegal activities (e.g. spamming, profile cloning, profile hijacking) that take advantage of their popularity. One tool used to detect these malicious activities is the social honeypot. In principle, social honeypots consist in honeypot profiles, for instance Facebook pages or Twitter accounts, which are able to attract users for further analysis. However, we are convinced that social honeypots can be seen not only as a cybersecurity countermeasure, but also as a flexible system that can be adopted for many different purposes. For instance, for customers profiling and products advertising, or for understanding social trends among people. This thesis aims to make a first attempt toward better understanding of the methodologies and technologies to build automated social honeypots on Instagram. This approach has never been exploited before, in fact there is no previous work that proposed social honeypots on this social network and, furthermore, all the social honeypots presented in the literature are not automated. Hence, our experiment consists in 21 social honeypots, deployed on Instagram, whose management is completely automatic. To this end, we have implemented two post generation strategies: one involves simpler methods such as using stock images, the second is based on more complex processes by using the latest Machine Learning technologies. Each honeypot is equipped with an engagement plan that identify how it generates engagement with other users. Our results show that automatic social honeypots on Instagram are possible and that they can be customized according to our needs. We have demonstrated that the post generation strategy based on Machine Learning is not the best choice yet and that a simple interaction with other users, by just liking or commenting their posts, is the option to be preferred. Thanks to these results, we are convinced that the work presented in this thesis can pave the way to further researches and solutions.