DoS/DDoS Attack Dataset of 5G Network Slicing

5G Network slicing is one of the key enabling technologies that offer dedicated logical resources to different applications on the same physical network. However, a Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack can severely damage the performance and functionality of network slices. Furthermore, recent DoS/DDoS attack detection techniques are based on the available data sets which are collected from simulated 5G networks rather than from 5G network slices. In this paper, we first show how DoS/DDoS attacks on network slices can impact slice users' performance metrics such as bandwidth and latency. Then, we present a novel DoS/DDoS attack dataset collected from a simulated 5G network slicing test bed. Finally, we showed a deep-learning-based bidirectional LSTM (Long Short Term Memory) model, namely, SliceSecure can detect DoS/DDoS attacks with an accuracy of 99.99% on the newly created data sets for 5G network slices.