Trends in Existing and Emerging Cyber Threat Intelligence Platforms

The purpose of this paper is to present comparative analysis of cyber threat intelligence platforms and their features. This work include comparative analysis of existing ontologies for cyber threat collectors/sensor, data enrichment and data analytical techniques used for raw data analysis and community models for sharing cyber threats, intelligence and countermeasures. Firstly, this work performs comparative analysis of various data sensors designed for collecting raw data from different networks: wired, wireless and mobile. Secondly, detail analysis is performed on various interfaces designed to map ontologies into schemas. Thirdly, efficient methods for data analysis are considered for comparative and detailed report. These method extracts threat information from raw data. Lastly, various cybersecurity community models are analyzed with an aim of identifying an efficient cyber threat sharing model. It is observed that ontology based data sensor mechanisms are more efficient as compared to taxonomy models. It helps in identifying various cyber threats in stipulated time period. In another observation, it is found that decision tree based data analytical techniques are more effic