Combining a triplet autoencoder with DBSCAN centroid thresholds improves detection of unseen malware families in two datasets, but the temporal evaluation protocol and unreported hyperparameters undermine the results.
Novel feature extraction, selection and fusion for effective malware family classification,
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Addressing malware family concept drift with triplet autoencoder
Combining a triplet autoencoder with DBSCAN centroid thresholds improves detection of unseen malware families in two datasets, but the temporal evaluation protocol and unreported hyperparameters undermine the results.