Hifoots: A Highly Efficient DDoS Attack Detection Scheme Deployed in Smart IoT Homes

Abstract

This paper concerns the detection of Distributed Denial of Service (DDoS) attacks in network traffic generated by Internet of Things (IoT) devices in smart home environments. The detection of DDoS attacks is crucial for IoT network security, as such attacks can disrupt the availability of essential services. In particular, due to the growing popularity of smart homes and the emergence of malicious software that compromises devices, home IoT devices have become susceptible to botnet infections capable of launching DDoS attacks. With the development of artificial intelligence technology, many advanced methods have been proposed that show promising performance in detecting DDoS attacks. However, there is still a need for improvement in their generalizability and detection efficiency. In this paper, we propose Hifoots, a highly efficient IoT DDoS attack detection scheme, aiming to achieve high detection robustness and detection efficiency. Hifoots builts upon our key observation that DDoS attacks can be detected by examining the group behavior of all flows over a given time interval. We evaluated Hifoots on five complex DDoS attack scenarios. The experimental results demonstrate that Hifoots outperforms the detection performance of existing state-of-the-art methods and offers an improvement in detection efficiency that is up to 12 times better, along with stronger generalizability compared to the state-of-the-art methods.