Forum Numerica - Dr Pedro Casas: AI4SEC - Enhancing Cybersecurity through AI/ML


We are losing the battle against cybercrime.
If cybercrime would be measured as a country, in 2021 it would be the third largest economy behind US and China, and cybercrime could cost more than 10 trillion USD by 2025. The cyberattack surface growth is outpacing humans’ ability to secure it.
The impressive success of Artificial Intelligence (AI) and Machine Learning (ML) in multiple data-driven problems over the past decade has motivated a flourishing research domain targeting the application of AI/ML to cybersecurity problems - AI4SEC. However, making of AI4SEC an accepted and fruitful approach to cybersecurity in the practice is challenging.
In this presentation, I elaborate on the most important technical show-stoppers in AI4SEC, presenting an overview on the work we have been developing over the last decade on the application of AI/ML to network security to improve the state of affairs in cybersecurity.
The goal of the talk is to motivate – for the newcomers – and to strengthen – for those already in the field – the research in AI4SEC.

About the speaker

Dr. Pedro Casas is a Senior Scientist at the AIT Austrian Institute of Technology, within the Data Science and Artificial Intelligence (DSAI) competence unit.
He is an expert and lead researcher in AI4NETS (AI/ML for Networks), leading multiple national and international projects in network measurement and data analytics. Before joining AIT, he was Senior Researcher at the FTW Telecommunications Research Center Vienna, leading research activities in network traffic monitoring and analysis.
He holds a PhD in computer science from Télécom Bretagne (France), and a PhD in electrical engineering from Universidad de la República (Uruguay).
He has published more than 180 Networking research papers in major international conferences, journals, and workshops, received 14 best paper, best demo, best student work, and best workshop awards, and periodically participates as chair for different conferences and workshops in network measurement and analysis.
His main research areas include machine-learning based approaches for Networking, network security and anomaly detection, big data analytics and platforms, Internet network measurements, as well as QoE modeling, assessment, and monitoring.