Advanced Cybersecurity Frameworks Using Team Optimization Algorithms and Convolutional Recurrent Neural Networks

Abstract

In response to escalating cybersecurity threats, this paper explores an innovative framework combining team optimization algorithms and Convolutional Recurrent Neural Networks (CRNNs) to enhance cybersecurity measures. Traditional cybersecurity frameworks often face challenges in effectively handling the dynamic and complex nature of modern threats. By integrating team optimization algorithms, such as genetic algorithms and ant colony optimization, with CRNNs capable of processing both sequential and spatial data, this study proposes a robust solution. The framework aims to improve detection accuracy, response time, and overall resilience against sophisticated cyber attacks. Through empirical evaluations and case studies, we demonstrate the effectiveness and versatility of the proposed approach in real-world cybersecurity applications.