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Found 3 papers, 2 papers with code
One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning
In particular, we utilize supervised contrastive learning to enhance the packet-level and flow-level representations and perform graph data augmentation on the byte-level traffic graph so that the fine-grained semantic-invariant characteristics between bytes can be captured through contrastive learning.
Activation functions are not needed: the ratio net
We compare the effectiveness and efficiency of the ratio net and that of the RBF and the MLP with various kinds of activation functions in the classification task on the mnist database of handwritten digits and the Internet Movie Database (IMDb) which is a binary sentiment analysis dataset.
IoT Security: An End-to-End View and Case Study
The work in this paper raises the alarm again for the IoT device manufacturers to better secure their products in order to prevent malware attacks like Mirai.
Cryptography and Security
