An alarming number of cyber attacks have been witnessed in recent years, especially during COVID-19 epidemic. Many prevention measures have been proposed as a defense line for a network infrastructure, including Intrusion Detection System (IDS). A variance of IDS using Machine Learning and Deep Learning to detect network anomalies is gaining promising results. However, this approach also poses limitations regarding the indigenous dataset acquisition or the ability to apply a model learned from a benchmark dataset to different network infrastructures. Therefore, this paper proposes a reliable automatic labeling method for a new network dataset, and a Deep Transfer Learning model to detect both known and unknown attacks across different network infrastructures, then compares with other approaches. Network attacks detection using auto-labeling and network-based Deep Learning The obtained results reveal an outstanding performance of Transfer-Learning model, in comparison with non Transfer-Learning method, on two benchmark datasets (NSL-KDD, CIC-IDS2017) and a self-captured simulated network dataset using the auto-labeling approach. Furthermore, the model is verified to retain both new and old knowledge after the transfer learning process, which has not been mentioned in other studies, focusing only on learning new knowledge ability.

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