DTL-IDS: Deep Transfer Learning-Based Intrusion Detection System in 5G Networks

• Published in: 2023 19th International Conference on Network and Service Management (CNSM) pp. 1 - 5
• Main Authors: Farzaneh, Behnam, Shahriar, Nashid, Al Muktadir, Abu Hena, Towhid, Md. Shamim
• Format: Conference Proceeding
• Language: English
• Published: IFIP 30.10.2023
• Subjects: 5G mobile communication; 5G Networks; Boosting; Data models; Deep learning; Deep Transfer Learning; Intrusion Detection; Network intrusion detection; Training; Transfer learning
• ISSN: 2165-963X
• DOI: 10.23919/CNSM59352.2023.10327918

In the complex landscape of modern networks, the necessity of Intrusion Detection System (IDS) has become paramount. An IDS is a crucial cybersecurity tool that plays a pivotal role in safeguarding networks against a wide array of threats and attacks. The application of deep learning models for intrusion detection is becoming popular among research communities due to its success in many other domains. However, deep learning models require a significant amount of labeled data to achieve effective training. Obtaining labeled data for intrusion detection can be challenging and costly. To address it, Deep Transfer Learning (DTL) can be employed. This research introduces an innovative traffic classification method tailored for 5G networks. The approach leverages deep transfer learning by utilizing pre-trained models and fine-tuning them. We evaluate several deep-learning models in a transfer learning setting. The Inception model being identified as the top-performing model shows an improvement of approximately 10% in terms of F1-score between IDS-based DTL and the same scheme without DTL.