One effort to secure vehicle-to-vehicle (V2V) communication is to use a symmetrical cryptographic scheme that requires the distribution of shared secret keys. To reduce attacks on key distribution, physical layer-based key formation schemes that utilize the characteristics of wireless channels have been implemented. However, existing schemes still produce a low bit formation rate (BFR) even though they can reach a low bit error rate (BER). Note that V2V communication requires a scheme with high BFR in order to fulfill its main goal of improving road safety. In this research, we propose a higher rate secret key formation (HRKF) scheme using received signal strength (RSS) as a source of random information. The focus of this research is to produce keys with high BFR without compromising BER. To reduce bit mismatch, we propose a polynomial regression method that can increase channel reciprocity. We also propose a fixed threshold quantization (FTQ) method to maintain the number of bits so that the BFR increases. The test results show that the HRKF scheme can increase BFR from 40% up to 100% compared to existing research schemes. To ensure the key cannot be guessed by the attacker, the HRKF scheme succeeds in producing a key that meets the randomness of the NIST test.
Vehicular ad-hoc network is an exciting study that aims to improve driver safety in driving. Vehicle-to-vehicle (V2V) is communications between vehicles that occurs on a VANET using wireless channels. This channel allows vehicles to share personal or safety information with other vehicles. Vehicle communication is potentially vulnerable to adversaries' security attacks that can harm the driver and other legitimate users. Therefore, it requires a high-security system. This research proposes a new scheme, namely the MAPI (Mike-Amang-Prima-Inka), as a modified secret key generation scheme obtained from received signal strength (RSS) values. Our research focuses on obtaining a symmetric key that has a high key formation speed (KFS) with a low-key discrepancy level (KDL), while still thinking about the randomness and ensure safety from passive attackers. In the pre-processing, we use a combination of Kalman Filter and Polynomial Regression by modifying the parameters to produce the best performance. We also modified the grey code in the Modified Multibit (MMB) Quantization method to reduce the quantization bit mismatch. Our approach to the MAPI scheme can assign symmetric keys with better performance than existing schemes, increasing KFS and decreasing KDL up to 100%. Moreover, the scheme can generate a symmetric key that deals with NIST's statistical tests.
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