Fuzzy autoencoder improves containment of adversarial malware
A fuzzy autoencoder classified none of 10,000 adversarially generated executable samples as legitimate and reached a reported safety-aware accuracy of ...
A fuzzy autoencoder classified none of 10,000 adversarially generated executable samples as legitimate and reached a reported safety-aware accuracy of ...
A hierarchical attention model reported 0.913 detection accuracy, 83.6% zero-shot attack recognition and 29.8-millisecond average detection latency in computational experiments.
A blockchain-integrated machine-learning framework reported accuracies above 99% for distinguishing attacks from normal activity in two IoHT healthcare-device datasets.
Researchers have developed CSCProv, a compression framework that shrinks massive provenance graphs by up to thirty times while preserving the ...
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© 2025 Scienmag - Science Magazine