MBDMMarketing and Business Development Manager (various companies)
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Then MBDM adaptively adjusts the gradient for backpropagation according to loss cache et.
Here, we make some experiments to explore the performance of HDN compared with standard LSTM model, we also evaluate the effect of MBDM and two opcode encoding methods to better optimize HDN model.
Moreover, we compare HDN with or without MBDM to verify whether MBDM can effectively remove the noise in sequence and enhance overall detection performance.
As can be seen from the results, MBDM brings a considerable improvement on detection rate for HDN proving that using MBDM to denoise opcode sequences is effective.
For each subdataset, we use three methods (standard LSTM, HDN, and HDN (no MBDM)) to experiment on all the subdatasets separately.
And the use of MBDM brings an obvious improvement when opcode sequence length is greater than 1,000.
From the above experiments we know that MBDM helps HDN get a better detection result through subsequence denoising, we intend to further explore and optimize the hyperparameters for MBDM.
In this paper, we evaluate five different filtering ratio ratios [sigma] (= 0%, 1%, 2.5%, 5%, 10%), where [sigma] = 0% means that MBDM is not enabled.
As mentioned above, choosing the filtering ratio for MBDM requires a trade-off between data quality and model generalization performance.
For the noisy segments in opcode sequences, HDN uses MBDM for denoising processing.
Caption: Figure 9: The comparison for different filtering ratios a for MBDM.