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These days, utilization of Transfer Learning and more specifically Domain Adaptation is increasingly getting researchers' attention specifically for solving real-world problems. Network security applications including intrusion detection and network traffic classification can also make a profit from this technique. As a general definition, transfer learning methods extract knowledge from one domain(Source) and employ them for solving the problem in another domain(Target). The definition of the domain depends on the problem to be solved. Particularly, in the network field, we can refer to each separate network as a domain. So, using Transfer Learning, it would be possible to use the available(labeled) samples from one network to train a learning model in another network. Despite the fact that there are many developed transfer learning methods, the utilization of that in the network security fields is not investigated enough yet. The main objective of this work is 1)to study the challenges behind the use of the Transfer learning in network security applications 2) to study the different transfer learning techniques including instance_based, feature_based, and model_based methods that can be applied in this field, and finally 3) develop a new transfer learning method for this field.
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