7,953 research outputs found
A Real-Time Remote IDS Testbed for Connected Vehicles
Connected vehicles are becoming commonplace. A constant connection between
vehicles and a central server enables new features and services. This added
connectivity raises the likelihood of exposure to attackers and risks
unauthorized access. A possible countermeasure to this issue are intrusion
detection systems (IDS), which aim at detecting these intrusions during or
after their occurrence. The problem with IDS is the large variety of possible
approaches with no sensible option for comparing them. Our contribution to this
problem comprises the conceptualization and implementation of a testbed for an
automotive real-world scenario. That amounts to a server-side IDS detecting
intrusions into vehicles remotely. To verify the validity of our approach, we
evaluate the testbed from multiple perspectives, including its fitness for
purpose and the quality of the data it generates. Our evaluation shows that the
testbed makes the effective assessment of various IDS possible. It solves
multiple problems of existing approaches, including class imbalance.
Additionally, it enables reproducibility and generating data of varying
detection difficulties. This allows for comprehensive evaluation of real-time,
remote IDS.Comment: Peer-reviewed version accepted for publication in the proceedings of
the 34th ACM/SIGAPP Symposium On Applied Computing (SAC'19
Road Friction Estimation for Connected Vehicles using Supervised Machine Learning
In this paper, the problem of road friction prediction from a fleet of
connected vehicles is investigated. A framework is proposed to predict the road
friction level using both historical friction data from the connected cars and
data from weather stations, and comparative results from different methods are
presented. The problem is formulated as a classification task where the
available data is used to train three machine learning models including
logistic regression, support vector machine, and neural networks to predict the
friction class (slippery or non-slippery) in the future for specific road
segments. In addition to the friction values, which are measured by moving
vehicles, additional parameters such as humidity, temperature, and rainfall are
used to obtain a set of descriptive feature vectors as input to the
classification methods. The proposed prediction models are evaluated for
different prediction horizons (0 to 120 minutes in the future) where the
evaluation shows that the neural networks method leads to more stable results
in different conditions.Comment: Published at IV 201
Open Platforms for Connected Vehicles
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