CIVID - Collaborative In-vehicle Intrusion Detection
Typ
Examensarbete för masterexamen
Program
Software engineering and technology (MPSOF), MSc
Publicerad
2021
Författare
Aryan, Daniel
Söderberg, Kristoffer
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Context: Developments within the automotive domain have caused an increased
vulnerability to attacks against connected vehicles. To mitigate this threat, vehicles
utilize intrusion detection systems.
Problem: Intrusion detection systems can be very effective in detecting and stopping
ongoing attacks against vehicles. However, these systems are not infallible and would
benefit from increased accuracy in their attack detection.
Objective: By leveraging the access that these connected vehicles have to other ve hicles and the outside world, this thesis has designed and evaluated a framework for
collaborative intrusion detection (CIVID) with the stated goal of increasing detec tion accuracy.
Approach: A design science methodology has been applied to conceptualize the
problem, design a solution and validate this solution through the simulation of a
virtual vehicle fleet.
Result: The aforementioned validation of the collaborative framework shows a
marginal increase in accuracy measures through the utilization of a collaborative
intrusion detection approach. However, the results also show that the implemen tation of CIVID yields increased time-to-detection of security events that require
consultation.
Conclusion: Despite showing increased accuracy measures, it is unclear whether or
not the costs and risks associated with the CIVID framework outweigh the marginal
improvements in accuracy measures that it provides. Also, there are many additional
challenges that need to be dealt with when implementing the CIVID framework, such
as trust- and resource management. How these are to be implemented as well as
alternative implementations of the CIVID framework, is left to be explored in future
research.
Beskrivning
Ämne/nyckelord
Automotive , AUTOSAR , Anomaly-based detection , Collaborative IDS In-vehicle networks , Intrusion detection systems