This paper presents several novel algorithms for real-time cyberattack detection using the Auto-Associative Deep Random Neural Network, which were developed in the HORIZON 2020 IoTAC Project. Some of these algorithms require offline learning, while others require the algorithm to learn during its normal operation while it is also testing the flow of incoming traffic to detect possible attacks. Most of the methods we present are designed to be used at a single node, while one specific method collects data from multiple network ports to detect and monitor the spread of a Botnet. The evaluation of the accuracy of all the methods is carried out with real attack traces. These novel methods are also compared with other state-of-the-art approaches, showing that they offer better or equal performance, at lower computational learning and shorter detection times as compared to the existing approaches.

翻译：本文介绍了在HORIZON 2020 IoTAC项目框架下开发的多项基于自联想深度随机神经网络的新型实时网络攻击检测算法。部分算法需要离线学习，而另一些则要求算法在正常运行过程中，同时测试入站流量以检测潜在攻击时进行在线学习。我们所提出的方法大多设计为在单一节点上运行，其中一种特定方法通过收集多个网络端口的数据来检测并监控僵尸网络的传播。所有方法的准确性评估均基于真实攻击轨迹进行。这些新型算法还与当前其他先进方法进行了比较，结果表明，与现有方法相比，它们在保持相同或更优性能的同时，实现了更低计算开销的学习过程与更短的检测时间。