Denial of service attacks pose a threat in constant growth. This is mainly due to their tendency to gain in sophistication, ease of implementation, obfuscation and the recent improvements in occultation of fingerprints. On the other hand, progress towards self-organizing networks, and the different techniques involved in their development, such as software-defined networking, network-function virtualization, artificial intelligence or cloud computing, facilitates the design of new defensive strategies, more complete, consistent and able to adapt the defensive deployment to the current status of the network. In order to contribute to their development, in this paper, the use of artificial immune systems to mitigate denial of service attacks is proposed. The approach is based on building networks of distributed sensors suited to the requirements of the monitored environment. These components are capable of identifying threats and reacting according to the behavior of the biological defense mechanisms in human beings. It is accomplished by emulating the different immune reactions, the establishment of quarantine areas and the construction of immune memory. For their assessment, experiments with public domain datasets (KDD'99, CAIDA'07 and CAIDA'08) and simulations on various network configurations based on traffic samples gathered by the University Complutense of Madrid and flooding attacks generated by the tool DDoSIM were performed.

翻译：拒绝服务攻击的威胁持续增长，这主要源于其日益复杂化、易于实施、混淆性增强以及指纹隐匿技术的近期进步。另一方面，自组织网络及其开发所涉及的不同技术（如软件定义网络、网络功能虚拟化、人工智能或云计算）的进展，有助于设计更全面、更一致且能根据网络当前状态调整防御部署的新型防御策略。为促进其发展，本文提出使用人工免疫系统来缓解拒绝服务攻击。该方法基于构建适合被监控环境需求的分布式传感器网络。这些组件能够识别威胁，并根据人类生物防御机制的行为做出反应。这是通过模拟不同的免疫反应、建立隔离区域和构建免疫记忆来实现的。为评估该方法，我们使用公共数据集（KDD'99、CAIDA'07 和 CAIDA'08）进行了实验，并基于马德里康普顿斯大学收集的流量样本及工具 DDoSIM 生成的泛洪攻击，在多种网络配置上进行了仿真。