Authors: G. Preethi, B. Pravalika
Abstract: Network information transmission security has become more complicated due to the ever-increasing complexity of digital communication infrastructures and the ever-increasing volume of network traffic. When dealing with large-scale data settings, traditional methods of network security monitoring may be inefficient and hard to adjust to. In light of these difficulties, this research proposes an LSH-based data-driven paradigm for situational awareness in network security. The suggested architecture analyses and monitors network events in real-time to spot suspicious patterns that might represent security risks. In order to effectively find similar patterns and detect anomalous occurrences, the LSH algorithm is used after data preparation and feature analysis to the network information. To further assess its efficacy in security scenario awareness, the suggested method is compared to the Bayesian algorithm. The LSH algorithm outperforms the Bayesian method, which yields a detection rate of 80% to 85%, according to experimental study, which falls anywhere between 90% and 95%. Furthermore, the LSH-based method consistently identifies anomalies in networks with a fidelity and accuracy that surpasses that of traditional methods, with a false detection rate that remains below 1%. By maintaining the original methodology and experimental evaluation, the proposed framework efficiently improves the security of network information transmission by helping to identify threats faster, increasing situational awareness, and making large-scale communication environments more resilient.
DOI: http://doi.org/10.5281/zenodo.21375863
