Creating a comparison and alert methodology for managing the CCTX feed

Most collaborations and government departments share their threat data feed in Data Exchange. Inescapably, nowadays with increasing threat data, it is a challenge to extract a large amount of threat data and unify the format more quickly. And as more and more companies join in sharing, the redundancy of this duplicate data will increase dramatically. This project proposes machine learning algorithms for automatic format conversion to extract threat information from the traffic data, and convert them into STIX format and detect whether these structured feeds already exist in CCTX. And a dashboard is developed for security analysts to compare the frequency in feeds.

Faculty Supervisor:

Ali Dehghantanha

Student:

Yangyi Zou

Partner:

Canadian Cyber Threat Exchange

Discipline:

Computer science

Sector:

Other services (except public administration)

University:

University of Guelph