  The internet-of-Vehicle (IoV) can facilitate seamless connectivity between
connected vehicles (CV), autonomous vehicles (AV), and other IoV entities.
Intrusion Detection Systems (IDSs) for IoV networks can rely on machine
learning (ML) to protect the in-vehicle network from cyber-attacks.
Blockchain-based Federated Forests (BFFs) could be used to train ML models
based on data from IoV entities while protecting the confidentiality of the
data and reducing the risks of tampering with the data. However, ML models
created this way are still vulnerable to evasion, poisoning, and exploratory
attacks using adversarial examples. This paper investigates the impact of
various possible adversarial examples on the BFF-IDS. We proposed integrating a
statistical detector to detect and extract unknown adversarial samples. By
including the unknown detected samples into the dataset of the detector, we
augment the BFF-IDS with an additional model to detect original known attacks
and the new adversarial inputs. The statistical adversarial detector
confidently detected adversarial examples at the sample size of 50 and 100
input samples. Furthermore, the augmented BFF-IDS (BFF-IDS(AUG)) successfully
mitigates the adversarial examples with more than 96% accuracy. With this
approach, the model will continue to be augmented in a sandbox whenever an
adversarial sample is detected and subsequently adopt the BFF-IDS(AUG) as the
active security model. Consequently, the proposed integration of the
statistical adversarial detector and the subsequent augmentation of the BFF-IDS
with detected adversarial samples provides a sustainable security framework
against adversarial examples and other unknown attacks.
