Design of an Efficient Multidimensional Feature Analysis Deep-Learning Model for Cross Verification of Packet Source in Blockchain Deployments

Abstract

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. This paper presents an efficient deep-learning model 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 then on the blockchain for secure and tamper-resistant record-storage operations. The implementation of an optimized Vector AutoRegression 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. The proposed model outperforms existing methods, improving source tracing precision by 4.9%, accuracy by 2.5%, and recall by 3.5%. 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 research contributes to the improvement of network security and system efficiency, surpassing the limitations of existing methods and paving the way for future developments in blockchain technology process.