| KEYWORD |
DAUIN - GR-24 - SMILIES - reSilient coMputer archItectures and LIfE Sci
Social media-based security analysis applied to the Threat risk assessment
Parole chiave AUTOMOTIVE, DEEP LEARNING, NIS2.0, REAL TIME, SAFETY, SECURITY, THREAT MODELING
Riferimenti STEFANO DI CARLO, ALESSANDRO SAVINO
Riferimenti esterni OBERTI FRANCO
Gruppi di ricerca DAUIN - GR-24 - SMILIES - reSilient coMputer archItectures and LIfE Sci
Tipo tesi APPLICATIVA, RICERCA APPLICATA, RICERCA SPERIMENTALE, SVILUPPO HARDWARE
Descrizione Scenario:
The European Union (EU) is imposing more and more new directivities concerning type approval on product security. If the UNR 155 and UNR156 are specific to the road vehicle domain, the new NIS 2.0 covers a broader audience of critical infrastructure, including energy, transport, banking, health, supply, and distribution. In this paradigm, security is no longer an extra cost but a value for the product. The industries need to shake up the current methodologies to get a more accurate risk analysis assessment based on the realistic index and no longer on individual judgment.
Thesis’ activities:
The student shall develop a Natural Language Processing (NLP) with a focus specially on the use of Artificial Neural Network for text classification and sentimental analysis. This social medial analysis goal is to raise emerging threats and well-liked and hot attacks applied to road vehicles domains to aid the risk assessment evaluation.
Note In collaboration with PUNCH Softronix
Scadenza validita proposta 23/01/2024
PROPONI LA TUA CANDIDATURA