Research on network security situational awareness prediction based on enhanced honey badger algorithm

Due to the extensive intertwining of the Internet and information systems, cybersecurity risks have markedly increased, necessitating a shift from safeguarding information to comprehensive cybersecurity. In response to the challenges posed by the high expenses and inefficiencies of traditional feature selection techniques, this study introduces an optimized Honey Badger Algorithm with reduced computational overhead (EHBA), coupled with Random Forest (RF) technology, specifically tailored for feature selection in cybersecurity datasets. This method not only boosts the precision and effectiveness of feature selection but also diminishes reliance on data, bolsters the model's ability to generalize, and efficiently manages computational expenses. The model promptly captures the current cybersecurity landscape and forecasts security trends, thereby providing robust support in addressing intricate cybersecurity dilemmas. Empirical findings substantiate its substantial efficacy in mitigating the escalating cybersecurity concerns.