# Studying the Impact of a UDP DoS Attack on the Parameters of VoIP Voice and Video Streams
## 2 Related Works  
In [12], the authors provide an in-depth review of the feasibility of using deep learning techniques for detecting DDoS attacks within VoIP networks. Particular attention is paid to Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) models. Through an in-depth analysis, the authors examined these approaches on various parameters, focusing on their main strengths and weaknesses. Furthermore, they conducted a study aimed at improving the efficiency and accuracy of these methodologies, thus strengthening the resilience of the VoIP networks against malicious attacks. In [13], the authors provide a sub-analysis and study of the security and vulnerability of VoIP networks, as well as an analysis of the existing level of network security and its impact on VoIP network performance and voice quality. This work is mainly aimed at Penetration Testers. The attacks considered by the authors are denial of service (DoS) attacks, Registration Manipulation and Hijacking Authentication Attacks, Caller ID Spoofing, Man-in-the-middle attacks, Virtual Lan Hopping, Passive and Active Eavesdropping, Spamming over Internet Telephony (SPIT) and VoIP Phishing (Vishing). In [14], the authors reviewed various technologies and methods for warning and preventing attacks. The authors discussed various vulnerabilities associated with VoIP as well as tests to detect these vulnerabilities that are used in various VoIP network security inspection tools. These tools are used to detect the weaknesses/vulnerabilities that create security threats and problems in VoIP networks. In [15], the authors propose a P2P VoIP system in which the management of user data is implemented using distributed blockchain technology. The carried-out research shows that this approach can enhance security and decentralization in P2P VoIP. Additionally, the authors thoroughly evaluate the performance and security of the proposed blockchain, the consensus algorithm and several additional blockchain architectures that will be used to manage this data. The authors' proposed method offers promising prospects for the future of P2P VoIP. In [16], the authors carried out research in the area of Syn flood and UDP Lag attack detection based on designing and building a system using a machine learning algorithm that incorporates ensemble learning using AdaBoost and the CICDDoS2019 dataset. The sequence to be followed to detect SYN flood and UDP Lag attacks is system design, data collection, search and data analysis. Metrics such as accuracy, recall and F1-score are used to detect distributed denial-of-service (DDoS) attacks including SYN flood and UDP Lag attacks. In [17], the authors propose a widely applicable P4-based SDN secure system. Through the experiments carried out, the authors show that the proposed secure system can effectively reduce the impact of SYN flooding and UDP flooding. For SYN flooding, the proposed system can release server resources six times earlier if the proposed system is used. During the UDP flooding, the proposed system can reduce the amount of malicious traffic by approximately two-thirds if the proposed system is used again. In [18], the authors created a machine-learning-based model to predict multiple and different DDoS attacks. The machine learning models used to classify these attacks were Logistic Regression, K-nearest neighbor (KNN), Multi-Layer Perceptron and Decision Tree classifiers. The implementation of the proposed model was carried out using Jupyter Notebook and Python. Based on the carried out experimental studies, KNN and Decision Tree classifiers showed almost similar and best accuracy of 99.98% in predicting TCP and ICMP flooding attacks. The Decision Tree classifier showed the best accuracy of 77.23% compared to others in predicting UDP flooding.