In the era of Internet of Things (IoT) powered by 5G technologies, Automobile Industry is headed towards a revolution. In Intelligent Transport Systems (ITS), vehicles act as connected entities, and exchange data with each other and with the back-end servers on the mobile network. These communications are often session based and require a light weight protocol for session establishment and continuity. Session Initiation Protocol (SIP) can act as the base for this kind of communication. However, its simplicity also makes the protocol vulnerable to various web attacks such as identity theft and Distributed Denial of Service (DDoS). As 5G technologies will enable high data rates to the users, this will also exponentially increase the threat of high-speed DDoS on the servers originating from different sources. Thus, appropriate solutions need to be developed for securing SIP systems from these threats. Machine Learning (ML) has transpired as a building block in cyber security solutions, and a large number of techniques are available to make quick and robust network defense systems by automating the identification of attack flows in the network. In this paper, a Deep Learning-based model is proposed for the identification and alleviation of DDoS attacks in SIP based networks. The work presented here uses a system that is scalable and highly available with load balancing and failover addressing capabilities. The datasets used for conducting experiments are created by emulating SIP sessions, generating DDoS attacks, capturing the normal and attack flows, and extracting time window-based features from the packets. A stacked autoencoder model is trained on the curated datasets to detect various types of DDoS attacks. Once an attack is detected, the Mitigation Policy Recommender module recommends various actions for threat mitigation. Performance of the system is assessed in terms of Accuracy, Precision, Recall and F1-Score. The proposed model obtains a significant improvement in the performance than the previously existing state-of-the-art techniques in terms of accuracy and detection rate.

在 5G 技术驱动的物联网 (IoT) 时代，汽车行业正走向一场革命。在智能交通系统 (ITS) 中，车辆充当连接实体，并相互交换数据，并与移动网络上的后端服务器交换数据。这些通信通常是基于会话的，并且需要用于会话建立和连续性的轻量级协议。会话发起协议 (SIP) 可以作为这种通信的基础。然而，它的简单性也使得该协议容易受到各种 Web 攻击，例如身份盗用和分布式拒绝服务 (DDoS)。由于 5G 技术将为用户提供高数据速率，这也将成倍增加来自不同来源的服务器上的高速 DDoS 威胁。因此，需要开发适当的解决方案来保护 SIP 系统免受这些威胁。机器学习 (ML) 已成为网络安全解决方案中的一个组成部分，并且有大量技术可用于通过自动识别网络中的攻击流来构建快速而强大的网络防御系统。在本文中，提出了一种基于深度学习的模型，用于识别和缓解基于 SIP 的网络中的 DDoS 攻击。这里介绍的工作使用具有负载平衡和故障转移寻址功能的可扩展且高度可用的系统。用于进行实验的数据集是通过模拟 SIP 会话、生成 DDoS 攻击、捕获正常和攻击流以及从数据包中提取基于时间窗口的特征来创建的。堆叠式自动编码器模型在精选数据集上进行训练，以检测各种类型的 DDoS 攻击。一旦检测到攻击，缓解策略推荐模块会推荐各种措施来缓解威胁。系统的性能根据准确度、精确度、召回率和 F1 分数进行评估。所提出的模型在准确性和检测率方面比以前存在的最先进技术在性能上获得了显着提高。