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  KEYWORD

CAN vehicle Network IDS using Neural Network

Parole chiave AUTHENTICATION, AUTOMOTIVE, CAN NETWORK, IDENTITY MANAGEMENT, INTRUSION DETECTION, NEURAL NETWORKS, 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 Automotive connectivity is no longer an afterthought. With the growth of complex customer features, the automotive Electronic Control Units (ECUs) are increasing their connectivity ability, not only Vehicle-to-Vehicle (V2V) but also Vehicle-to-Everything (V2X). In this condition, the automotive market is becoming the perfect playground for hackers who want to exploit in-vehicle network protocols’ vulnerabilities. The primary automotive communication protocol is the Controller Area Network (CAN). Limited hardware resources and real-time environment lack authentication and integrity mechanisms, making the vehicle network vulnerable.
In present activities, the student shall develop a CAN Intrusion Detection System (IDS) based on Neural Network plausibility data to improve today’s authentication and integrity mechanism system without affecting hardware crypto accelerations resources.

Conoscenze richieste Neural Networks

Note In collaboration with PUNCH Softronix


Scadenza validita proposta 16/12/2023      PROPONI LA TUA CANDIDATURA




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