Sökning: onr:"swepub:oai:DiVA.org:ri-65727" >
On the Resilience o...
On the Resilience of Machine Learning-Based IDS for Automotive Networks
-
- Zenden, Ivo (författare)
- RISE,RISE Research Institutes of Sweden
-
- Wang, Han (författare)
- RISE,Datavetenskap,RISE Research Institutes of Sweden
-
- Iacovazzi, Alfonso (författare)
- RISE,Datavetenskap,RISE Research Institutes of Sweden
-
visa fler...
-
- Vahidi, Arash (författare)
- RISE,RISE Research Institutes of Sweden
-
- Blom, Rolf (författare)
- RISE,Datavetenskap
-
- Raza, Shahid, 1980- (författare)
- RISE,Datavetenskap,RISE Research Institutes of Sweden
-
- Bolm, Rolf (författare)
- RISE Research Institutes of Sweden
-
visa färre...
-
(creator_code:org_t)
- IEEE Computer Society, 2023
- 2023
- Engelska.
-
Ingår i: proc of IEEE Vehicular Networking Conference, VNC. - : IEEE Computer Society. - 9798350335491 - 9798350335507 ; , s. 239-246
- Relaterad länk:
-
https://doi.org/10.1...
-
visa fler...
-
https://ri.diva-port... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
https://urn.kb.se/re...
-
visa färre...
Abstract
Ämnesord
Stäng
- Modern automotive functions are controlled by a large number of small computers called electronic control units (ECUs). These functions span from safety-critical autonomous driving to comfort and infotainment. ECUs communicate with one another over multiple internal networks using different technologies. Some, such as Controller Area Network (CAN), are very simple and provide minimal or no security services. Machine learning techniques can be used to detect anomalous activities in such networks. However, it is necessary that these machine learning techniques are not prone to adversarial attacks. In this paper, we investigate adversarial sample vulnerabilities in four different machine learning-based intrusion detection systems for automotive networks. We show that adversarial samples negatively impact three of the four studied solutions. Furthermore, we analyze transferability of adversarial samples between different systems. We also investigate detection performance and the attack success rate after using adversarial samples in the training. After analyzing these results, we discuss whether current solutions are mature enough for a use in modern vehicles.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- Adversarial AI/ML
- Controller Area Network
- Intrusion Detection System
- Machine Learning
- Vehicle Security
- Computer crime
- Control system synthesis
- Controllers
- Intrusion detection
- Learning algorithms
- Network security
- Process control
- Safety engineering
- Automotive networks
- Automotives
- Autonomous driving
- Controller-area network
- Electronics control unit
- Intrusion Detection Systems
- Machine learning techniques
- Machine-learning
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
Hitta via bibliotek
Till lärosätets databas