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Journal of Discrete Mathematical Sciences and Cryptography cover

Journal of Discrete Mathematical Sciences and Cryptography

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

CiteScore 2025: 2.7 (Q1)

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Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

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Journal of Discrete Mathematical Sciences and Cryptography
Open Access Research Article

Adaptive security reinforcement learning for enhanced cryptographic vehicle-to-vehicle communication security

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pp. 2499–2513Vol. 28Issue 6September 2025DOI: 10.47974/JDMSC-2335 Crossmark XML
Received:
12 Mar 2024
Published Online:
09 Jul 2025
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2335
Pages:
2499–2513

Abstract

Secure and reliable Vehicle-to-Vehicle (V2V) communications are critical in the age of connected vehicles and intelligent transport systems. This paper introduces a new technique, Adaptive Security Reinforcement Learning (ASRL), which aims at adjusting and learning cryptographic techniques according to effective on-the-fly information about vehicles. Marine security settings can be automatically adjusted via ASRL by using reinforcement learning to optimize set security settings, such as Elapsed Time, Speed, RPM, Acceleration, Engine Load, Fuel Level and Temperature, given changing conditions. The purpose of this adaptive approach is to keep safety and efficiency in balance, so that the overall security of vehicular communication can be improved without reducing operational performance. The solution involves multivariate polynomial lattices (MPL) for strong cryptographic underpinnings, quantum resilient cryptography for secure models against yet-to-become quantum-based threats, and discretized mathematical implies for optimized algorithms. The system also uses state-of-the art techniques to withstand algebraic and combinatorial attacks, including the use of designs and Boolean functions. The paper presents the conceptual framework of ASRL, by defining state space, action space and reward function in the idealized scenario and describing learning algorithm in detail, together with simulations to illustrate its effectiveness. Through this holistic treatment, we provide a working solution to evolving security threats in V2V communications, and hence we enable safer and more resilient vehicular networks.

Keywords

Adaptive securityReinforcement learningVehicle-to-vehicle communicationCryptographic securityMultivariate polynomial lattices (MPL)Quantum-resilient cryptographicDiscrete mathematicsAlgebraic attacks

Subject Classifications

Primary 68R01Secondary 97N70

References

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