Enhancing VoIP Security: Recent Advances in Deep Learning for DoS Detection

Distributed Denial of Service (DDoS) attacks pose a significant threat to Voice over Internet Protocol (V oIP) networks, presenting potential disruptions to both businesses and individuals. The inherently complex nature of VoIP data renders the detection of such attacks challenging. However, leveraging deep learning methodologies shows promise in addressing this challenge, given its ability to effectively model intricate patterns. This paper provides a comprehensive overview of the application of deep learning techniques for detecting DDoS attacks within VoIP networks. Specifically, we delve into Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) models, both widely utilized types of artificial neural networks. Through meticulous analysis, we scrutinize these approaches across various parameters, elucidating their primary strengths and weaknesses. Furthermore, we explore avenues of research aimed at enhancing the efficiency and accuracy of these methodologies, thereby bolstering the resilience of VoIP networks against malicious attacks.