Abstract
Mobile node authentication is crucial to providing secure data transmission in networking paradigms. The traditional models designed for mobile node authentication are vulnerable to security and scalability issues with increased computational complexity and memory requirements. The research tackles these challenges via the initiation of the Blockchain-enabled modified Lattice Cryptography-based Deep Neural Network-Adaptive Neuro Fuzzy Inference System (BC-mLC-DFIS). The blockchain with a smart contract mechanism enables secure data transmission and eliminates the scalability issues using its distributed ledger characteristics. Eventually, the intense linguistic variables of the fuzzy system present in DFIS enable accurate handover prediction and reduce the communication overhead. Further, the effective analysis of the adaptive multifactor aids in the active deployment of the mobile nodes after successful authentication using the Multifactor Adaptive Authentication-based Secure Hashing (MAA-SH). Moreover, the inclusion of the modified Lattice Cryptography(mLC) simulates higher data security using its low-degree polynomial characteristics. Numerical results obtained for the educational dataset in terms of 0.64ms decryption time, 122.33KB of memory usage, 0.84ms of encryption time, and 0.41ms time complexity show the superiority of the research for 250 users in comparison with traditional models.