Détection d'intrusion avec les techniques d'apprentissage en profondeur pour les réseaux automobiles embarqués
Natasha Al- Khatib · theses.fr (ABES) · 2023
In-vehicle communication which refers to the communication and exchange of data between embedded automotive devices plays a crucial role in the development of intelligent transportation systems (ITS), which aim to improve the efficiency, safety, and sustainability of transportation systems. The proliferation of embedded sensor-centric communication and computing devices connected to the in-vehicle network (IVN) has enabled the development of safety and convenience features including vehicle monitoring, physical wiring reduction, and improved driving experience. However, with the increasing complexity and connectivity of modern vehicles, the expanding threat landscape of the IVN is raising concerns. A range of potential security risks can compromise the safety and functionality of a vehicle putting the life of drivers and passengers in danger. Numerous approaches have thus been proposed and implemented to alleviate this issue including firewalls, encryption, and secure authentication and access controls. As traditional mechanisms fail to fully counterattack intrusion attempts, the need for a complementary defensive countermeasure is necessary. Intrusion Detection Systems (IDS) have been thus considered a fundamental component of every network security infrastructure, including IVN. Intrusion detection can be particularly useful in detecting threats that may not be caught by other security measures, such as zero-day vulnerabilities or insider attacks. It can also provide an early warning of a potential attack, allowing car manufacturers to take preventive measures before significant damage occurs. The main objective of this thesis is to investigate the capability of deep learning techniques in detecting in-vehicle intrusions. Deep learning algorithms have the ability to process large amounts of data and recognize complex patterns that may be difficult for humans to discern, making them well-suited for detecting intrusions in IVN. However, since the E/E architecture of a vehicle is constantly evolving as new technologies and requirements emerge, we propose different deep learning-based solutions for different E/E architectures and for various tasks including anomaly detection and classification.