Abstract:
A global network view-driven controller optimization can enhance the performance
of vehicular networks in a Software-Defined Vehicular Network (SDVN).
However, if Link Discovery (LD) Attacks (LDAs) contaminate the network architecture,
such performance may be significantly impacted. Dynamic SDVN LD is
less suitable for recent LDA mitigation techniques in the SDN realm. To address
this research gap, we first show that five LDAs may be implemented in a typical
SDVN architecture by taking advantage of flaws in the LD service and position
tracking. Our main contribution, however, is to propose a novel framework to
detect LDA by using a location-driven adaptive LD technique with Q-learning to
derive link states. To mitigate the attacks, we use Diffie-Hellman (DH) symmetric
key issuing, time-limited session-based Hash Message Authentication Codes
(HMACs), Zero-Knowledge Proofs (ZKPs) of HMACs stored in the blockchain,
packet limiting with signature matching, location verification, and cycle- and noncycle-
based digital signatures with a combination of post-quantum and classical
cryptography. Additionally, a permissioned blockchain is used by employing a
challenge-response mechanism for node registration with DH, global, and public
key exchange and subsequent node authentication through established encrypted
communication using signatures, ZKPs, attributes, and HMACs. A second smart
contract is used to maintain non-tamperable node reputation and location scores
by tracking node behavior and maintaining endorser trust scores for weighted
reputation updates for consensus-based node blacklisting. We evaluate all five
attack scenarios through simulations, and the findings indicate that the proposed
technique is exceptionally effective in countering the five link discovery attacks
outlined in this study, outperforming existing methodologies for SDN and Vehicular
Ad hoc Networks. It achieved the second-best channel utilization during
location-triggered flooding attack, the best link discovery latency during traditional
flooding attack, and optimal packet delivery ratio and Matthews correlation
coefficient, and zero packet interception ratios during fabrication, replay, and
link vanishing attacks, in spite of its increased computational complexity.
