Recently data science is running high. Advances in machine learning and semantic analysis can be used as alternative – or complementary – approaches to assisted data labelling. There is an increasing need for an automated labelling solutions, which can both support the users during the labelling process and be used to label the existing data sets.
Additionally, to perform an effective risk assessment of the security posture of a system, new robust system modelling and data analysis methods are required both to better understand historical data and past behaviour of the system. Ideally, this leads to the ability to predict the future security evolution of the system.
The end result of optimally analyzing the collected data can be applied to (existing) protection mechanism that organizations should have in order to achieve the "detective, protective and corrective" goal well set in cybersecurity frameworks. The speaker will give insight into what to use security big data analysis for and how to do that efficiently.
Anett Mádi-Nátor has more than a decade of experience in strategic and administrative layers of information security and cyber defense both as a private sector subject matter expert and as a government representative.
Her recent appointments include Hungarian MilCIRC Head of Coordination, Administrative Head of Hungarian government cyber security centre (Cyber Defence Management Authority within the National Security Authority), NATO Cyber Coalition Exercises Core Strategic and Administrative Planner, and Lead to NATO Cyber Defence Capability Team.
Up to the summer of 2015 Anett was the appointed primary policy and administrative contact point for Hungary in the Memorandum of Understanding in Cyber Defence between NATO and Hungary. Anett received a ministerial award for excelling public service in 2013.