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Seminario Cnr-Icar/4Securitas
Current cyber security systems are based on a “reactive” defense model, that is, they wait for the attack to take place and only then do they activate the defense and remediation strategies.

The ACSIA tool, on the other hand, is based on a proactive approach combined with ML and AI techniques. In practice, the system exploits the flaws present in the intrusion strategies to create the best prevention model, that is an unified real-time model with both reactive and proactive abilities.

This webinar, in addition to the description of ACSIA, will focus on an introduction to the approaches to cyberdefence, with particular attention to the application of ML and AI – used not to detect anomalies, but as an automation tool capable of remediation without human intervention, reducing false positives – and on the description, accompanied by a use case, of the two main models of cybersecurity development: proactive and reactive.



  • Introduction (Ing. Antonio Francesco Gentile, technologist at ICAR CNR, member of the IoT Edge Intelligence group and expert in Network Security over IOT)
  • R&D: fundamental factors for the creation of cyber solutions
  • Description of the different types of cyber attacks
  • Current cyber defense solutions and models and ML / AI role
  • Cyber attacks: recent use cases
  • New model and proactive IT security solutions

(Stefan Umit Uygur, 4Securitas)


Stefan Umit Uygur, Free Open Source Software evangelist, with a passion for sharing knowledge and twenty years of experience in the design of IT infrastructures and security systems. He is co-founder of 4Securitas, an innovative startup that has developed a next-generation cybersecurity monitoring, threat detection and mitigation solution for corporate servers (ACSIA).

How to participate

Wednesday 21 April 2021, hours 11:00 – 14:00 (CEST)

To join the meeting from a computer, tablet or smartphone:

URL via PC/Tablet
Access by telephone: +39 0 230 57 81 80

Access code: 761-612-877


Evento online


21/04/2021 11:00 am

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