Efficient AutoDL for Generating Denial-of-Service Defense Models in the Internet of Things

The Denial-of-Service (DoS) attacks have rapidly increased over the years, particularly from the Internet of Things (IoT) devices such as connected IP cameras/vehicles and IP door entries. As a result of a lack of strong security implementation, these low-cost IoT devices can become zombie bots by brute force (using a password dictionary) and malware injection. Thousands of such zombies have been a powerful tool to initiate DoS attacks that can consume any target network's bandwidth. Recently, Deep Learning (DL) based methods have achieved admirable performance to mitigate DoS attacks significantly. However, besides the high latency of processing huge volumes of incoming traffic, current D L-based methods are often designed based on crafting a model carefully, which costs the developers significant time and effort. This paper introduces a novel automated deep-learning (autoDL) scheme to automatically generate an efficient DoS defense model. The system can find the best suitable detection model and a detailed configuration that is lightweight enough to deploy at IoT gateways/wireless routers/programmable switches/edge servers near the attack sources. To our knowledge, this is the first attempt to develop such an autoDL platform for DoS filter generation. The evaluation results show that the defense system generated by autoDL can ease 99.7% of malicious traffic before they go out to the Internet with only 0.593 ms for request reaction, a promising performance compared to the literature.