| KEYWORD |
Malicious Code Detection for Automotive-secured systems
Parole chiave AUTHENTICATION, AUTOMOTIVE, INTRUSION DETETCTION, MALWARE, SECURE BOOT, SECURITY
Riferimenti STEFANO DI CARLO, ALESSANDRO SAVINO
Riferimenti esterni OBERT FRANCO
Gruppi di ricerca DAUIN - GR-24 - SMILIES - reSilient coMputer archItectures and LIfE Sci
Tipo tesi APPLICATIVA, RICERCA APPLICATA, RICERCA SPERIMENTALE, SVILUPPO HARDWARE
Descrizione The recent period is growing the number of attacks that leverage on relaying third parties’ software exploits. Many security vulnerability detection tools are already available today in the market. These tools allow scanning of the source code of projects, infrastructure and applications and reporting potential security vulnerabilities and weaknesses. The current approach swing between two categories of analyzers: dynamic and static. Both are not useful when the third-party software module is delivered to the customer directly compiled code without source code sharing. This habit is quite common in the automotive domain for protecting Intellectual Properties (IP), made easy by AUTOSAR Architecture Framework that guarantees software module compatibility.
In the present thesis, the student shall develop a novel static analysis based on traditional and deep-learning methods that detect potential software vulnerabilities on binary code instead of traditional source code.
Note In collaboration with PUNCH Softronix
Scadenza validita proposta 28/11/2023
PROPONI LA TUA CANDIDATURA