Trends and scientometrics in cyber security research

Abstract: To look for scientific literature, there are specialized databases and search engines to simplify the process. In order to quickly assess the quality of a given paper, there are different indices meant to quantify the success and impact an author has had within the scientific community. However, these indices have some flaws and could potentially be exploited. In this thesis, we aim to gather publication data from cyber security conferences, identify unknown patterns and trends as well as to introduce a new index or metric that better captures the impact of authors in the field than current common indices. We found that the cyber security community is in a healthy state with no obvious exploitation of common indices. With one notable exception, there is near equal distribution between citations within sub-communities and outside of them. We also found that the majority of authors with several publications chose to publish for several different conferences, not just one of them. Furthermore, new and growing trends in cyber security research were found to be ”machine learning”, ”blockchain” and ”differential privacy”. As for the conferences, it appears that USENIX has overtaken CCS in recent years as the conference with the highest publication output. While no attempts to exploit the common indices were identified, we believe that the risk is still there. We also identify other flaws with the usage of the common metrics in the cyber security research field. As such, we suggest the adoption of the pure R-index with a normalized proportional counting as the score calculation method, since it takes the number and order of the authors into consideration, as well as that it does not discriminate against authors with few publications with many citations.