We have previously created successful neural networks for malware detection. Here, we examine a network with salience to extract parts of an input deemed important. We show that the blocks we extract are what is important to the network, are unique to their class, and show clear similarities when clustered.
A Foray Into Extracting Malicious Features from Executable Code with Neural Network Salience
2019-07-01
1164456 byte
Conference paper
Electronic Resource
English
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