  Recurrent deep learning language models like the LSTM are often used to
provide advanced cyber-defense for high-value assets. The underlying assumption
for using LSTM networks for malware-detection is that the op-code sequence of
malware could be treated as a (spoken) language representation. There are
differences between any spoken-language (sequence of words/sentences) and the
machine-language (sequence of op-codes). In this paper, we demonstrate that due
to these inherent differences, an LSTM model with its default configuration as
tuned for a spoken-language, may not work well to detect malware (using its
op-code sequence) unless the network's essential hyper-parameters are tuned
appropriately. In the process, we also determine the relative importance of all
the different hyper-parameters of an LSTM network as applied to malware
detection using their op-code sequence representations. We experimented with
different configurations of LSTM networks, and altered hyper-parameters like
the embedding-size, number of hidden layers, number of LSTM-units in a hidden
layer, pruning/padding-length of the input-vector, activation-function, and
batch-size. We discovered that owing to the enhanced complexity of the
malware/machine-language, the performance of an LSTM network configured for an
Intrusion Detection System, is very sensitive towards the
number-of-hidden-layers, input sequence-length, and the choice of the
activation-function. Also, for (spoken) language-modeling, the recurrent
architectures by-far outperform their non-recurrent counterparts. Therefore, we
also assess how sequential DL architectures like the LSTM compare against their
non-sequential counterparts like the MLP-DNN for the purpose of
malware-detection.
