Cybersecurity remains a critical concern in today's digital landscape, where cyberattacks can have devastating consequences. Detecting cyberattacks in high-noise environments, characterized by a large volume of legitimate and illegitimate network traffic, poses a significant challenge. This study explores the use of solvent-assisted techniques, inspired by concepts in chemistry, to enhance cyberattack detection in such environments. By employing advanced machine learning algorithms and signal processing methods, this research aims to improve the accuracy and efficiency of cybersecurity measures. The findings highlight the potential of solvent-assisted techniques in effectively identifying cyber threats amidst noisy data, contributing to more robust cybersecurity frameworks. This study explores the enhancement of cyberattack detection in high-noise environments through the application of solvent-assisted techniques, leveraging advanced signal processing methods inspired by solvent extraction principles. The research introduces a novel approach that applies noise-filtering algorithms akin to solvent-assisted separation processes to distinguish between legitimate network traffic and potential cyber threats amidst high levels of background noise. By integrating these techniques with existing cybersecurity frameworks, the study demonstrates significant improvements in detecting and mitigating cyberattacks, reducing false positives, and improving overall system resilience. The findings suggest that adopting solvent-assisted methods in cybersecurity can enhance the accuracy and reliability of attack detection in complex and noisy network environments.