Abstract
In this paper we discuss an algorithm for transfer learning in cybersecurity. In particular, we develop a new image-based representation for the feature set in the source domain and train a convolutional neural network (CNN) using the training data. The CNN model is then augmented with one dense layer in the target domain before applying on the target dataset. The data we have used for our experimental results are taken from the Canadian Institute of Cybersecurity. The results show that transfer learning is feasible in cybersecurity which offers many potential applications including resource-constrained environments such as edge computing.
Partially supported by NSF grant# 1515358.
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Gangopadhyay, A., Odebode, I., Yesha, Y. (2020). A Domain Adaptation Technique for Deep Learning in Cybersecurity. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2019 Workshops. OTM 2019. Lecture Notes in Computer Science(), vol 11878. Springer, Cham. https://doi.org/10.1007/978-3-030-40907-4_24
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DOI: https://doi.org/10.1007/978-3-030-40907-4_24
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