A D3QN agent that jointly selects features and classifies malware reaches about 99% accuracy on two benchmarks, but the claimed efficiency gain fails because the full feature vector is always in the network input.
Optimizing malware de- tectionandclassificationinreal-timeusinghy- brid deep learning approaches
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Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification
A D3QN agent that jointly selects features and classifies malware reaches about 99% accuracy on two benchmarks, but the claimed efficiency gain fails because the full feature vector is always in the network input.