According to a recent report from Cisco, many organizations use VoIP services instead of their traditional telephone systems. VoIP solutions are replacing traditional systems worldwide and are expected to be the dominant voice communications technology for fifth-generation (5G) networks. Voice over IP (VoIP) is a collection of technologies and protocols used for transferring voice and multimedia over Internet Protocol (IP) networks. The detection accuracy was higher for low-rate attacks than that of traditional machine learning. Experiments show that the system has a high detection accuracy and low detection time. For validation, a balanced real traffic dataset was built containing three attack scenarios with different attack durations and intensities.
We discuss a deep learning model based on Recurrent Neural Networks (RNNs) developed to detect DDoS attacks with low and high-rate intensity. In this paper, we formulate detection of DDoS attacks as a classification problem and propose an approach using token embedding to enhance extracted features from SIP messages. The Distributed Denial of Service (DDoS) attack is a harmful attack, because it drains resources, and prevents legitimate users from using the available services. SIP is unprotected against attacks because it is a text-based protocol and lacks defense against the growing security threats. The Session Initiation Protocol (SIP) is a widely used protocol for handling VoIP signaling functions. Although implementation is simple, VoIP is vulnerable to different types of attacks. Many companies have transformed their telephone systems into Voice over IP (VoIP) systems.
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