Thesis supervisor: Zsolt Csaba Johanyák
Location of studies (in Hungarian): Obuda University - Bánki Donát Faculty of Mechanical and Safety Engineering Abbreviation of location of studies: OEBGK
Description of the research topic:
Modern vehicles are equipped with a wide variety of sensors, on- board computers and different devices supporting navigation and communication. These systems aim the fulfilment of various demands on the improvement of traffic safety, traffic/route optimization, passenger, comfort, etc. Inter-vehicle and vehicle-infrastructure communication plays an important role in this process, which resulted in the birth of Vehicular Ad-hoc Networks (VANETs).
In order to maintain a decent level of safety and privacy in vehicular networks, basic security requirements particularly availability, authentication, confidentiality, data integrity and non-repudiation have to be met. VANETs are exposed to several types of threats.
Through this project, we aim to develop machine learning and artificial intelligence based intrusion detection techniques that help to make VANETs safer.
Research purposes:
1. Identification of key features for the detection of specific attack types (feature selection)
2. Development of two different types of detection modules (e.g. based on fuzzy decision tree and deep neural networks)
3. Efficiency evaluation and comparison with results obtained by other (e.g. statistical) approaches
Required language skills: english C1 Further requirements: Python or Matlab programming experience
Number of students who can be accepted: 1
Deadline for application: 2023-02-28
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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