Artificial Bee Colony Algorithm Based Convolutional Neural Network for Cross Site Scripting Attacks Classification

Neuroevolution is a powerful method used to improve the performance of neural networks in various tasks by evolving their structure, weights, and complexity. It is often used in conjunction with evolutionary algorithms to design neural networks and enhance their architectures. There have been studies in the literature that demonstrate successful results using the neuroevolution approach in combination with different evolutionary algorithms. The Artificial Bee Colony (ABC) algorithm is a swarm intelligence-based evolutionary algorithm that aims to find the optimal solution in the search space of a known problem. In this study, for the first time, the ABC evolutionary algorithm is employed in the neuroevolution approach. Experimental results conducted with cross-site scripting (XSS) attack data show that the ABC-CNN method achieves successful results comparable to conventional methods in detecting XSS attacks.