Training school on “Digital forensics: evidence analysis via intelligent systems and practices.”

Budapest, Hungary | 5-9 October, 2020

Igor Kotsiuba took part in Round Table “Cybersecurity of OT and ICS” was held at Schneider Electrics Ukraine and organised by Industry 4.0 community

Igor Kotsiuba, invited as a lecturer in Training school on “Digital forensics: evidence analysis via intelligent systems and practices.” to be held in Budapest, Hungary 5-9 October, 2020

He will conduct a tutorial “Digital Forensic Readiness, practical frameworks for CyberSecurity & Investigation Capability Building” for the trainees will mainly be young students in order to achieve the needed knowledge and capacities in order to fostering the synergies among Digital Forensics and Artificial Intelligence/Automated Reasoning researchers and, in parallel, achieve the main goals of the Action.

The school is open to about 14 motivated members of the COST Action CA17124 and digital forensics community who are seeking advanced knowledge of digital forensics, machine learning and computational intelligence.

DigForAsp (Digital forensics: evidence analysis via intelligent systems and practices) – CA17124 is funded by the European Cooperation in Science and Technology (COST)

The Challenge of the proposed COST Action consists in creating a Network for exploring the potential of the application of Artificial Intelligence and Automated Reasoning in the Digital Forensics field, and creating synergies between these fields. Specifically, the challenge is to address the Evidence Analysis phase, where evidence about possible crimes and crimes perpetrators collected from various electronic devices (by means of specialized software, and according to specific regulations) must be exploited so as to reconstruct possible events, event sequences and scenarios related to a crime. Evidence Analysis results are then made available to law enforcement, investigators, public prosecutors, lawyers and judges: it is therefore crucial that the adopted techniques guarantee reliability and verifiability, and that their result can be explained to the human actors.