Strategy of a Resourceful Multidimensional Feature Study Deep-Learning Model for Cross Authentication of Packet Source in Blockchain Distributions
DOI:
https://doi.org/10.52783/jns.v14.2956Keywords:
Blockchain Deployments, Deep Learning Model, Frequency Component, Entropy Component, Z Transform, S Transform, Wavelet Components, VARMAx Model, ScenariosAbstract
Cross-verification of packet sources is a crucial process for sustaining the security and integrity of network communications, necessitated by the increasing prevalence of blockchain deployments across various technological sectors. Existing models, despite being functional, have a number of limitations, such as reduced precision in source tracing, suboptimal accuracy and recall rates, and significant processing delays. An effective deep learning model is presented in this research that facilitates enhanced cross-verification of packet sources in blockchain deployments. The proposed model makes use of multidomain features, namely Frequency, Entropy, Z Transform, S Transform, and Wavelet Components, which are subsequently stored on the blockchain for safe and impenetrable record-keeping. The implementation of an optimized Vector Auto-Regression Moving-Average with Exogenous Inputs (VARMAx) model forms the foundation of the tracing process. Source tracing is substantially more effective as a result of the VARMAx model's exceptional capacity for recognizing and predicting source patterns. A cross-verification mechanism that employs hash mapping in distributed environments further strengthens efficiency of the model for real-time deployments. This ensures the system's robustness and increases the reliability of packet source verification process. By increasing source tracing precision by 4.9%, accuracy by 2.5%, and recall by 3.5%, the suggested model beats current techniques. Additionally, it reduces the delay by 2.9%, optimizing the procedure as a whole for different scenarios. Through its novel and robust approach to packet source verification in blockchain deployments, this study improves system efficiency and network security by overcoming the shortcomings of current approaches and opening the door for further advancements in the blockchain technology process.
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