DeepSecure: Detection of Distributed Denial of Service Attacks on 5G Network Slicing-Deep Learning Approach

• Published in: IEEE wireless communications letters 2022-03, Vol.11 (3), p.488-492
• Main Authors: Kuadey, Noble Arden Elorm, Maale, Gerald Tietaa, Kwantwi, Thomas, Sun, Guolin, Liu, Guisong
• Format: Article
• Language: English
• Subjects: 5G mobile communication; 5G security; Cellular communication; Communications traffic; Computer crime; Cryptography; DDoS; Deep learning; Denial of service attacks; Denial-of-service attack; long short term memory; Machine learning; Network slicing; Predictive models; recurrent neural networks; Security management; Telecommunication traffic
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2021.3133479

Network slicing is one of the main enablers of the fifth-generation (5G) cellular network. However, it is susceptible to security threats such as distributed denial of service (DDoS) attacks. A DDoS attack on a slice could lead to the exhaustion of available common resources and a breach of the availability of resources on the slices. Recent works such as statistical, machine learning and cryptography techniques are limited by the requirement to define thresholds, feature engineering constraints and computation overload, respectively. In this letter, we propose DeepSecure, a framework based on a Long Short Term Memory deep learning technique that detects user equipment (UE) network traffic as DDoS attack or normal traffic and assigns an appropriate slice to a legitimate UE request. We compared our work with existing machine learning and deep learning techniques used in the literature. Experiment results showed that our proposed framework performed better in detecting DDoS attacks with an accuracy of 99.970% and predicting the appropriate slice requested by legitimate UE with an accuracy of 98.798%.