  Malicious software threats and their detection have been gaining importance
as a subdomain of information security due to the expansion of ICT applications
in daily settings. A major challenge in designing and developing anti-malware
systems is the coverage of the detection, particularly the development of
dynamic analysis methods that can detect polymorphic and metamorphic malware
efficiently. In the present study, we propose a methodological framework for
detecting malicious code by analyzing run trace outputs by Long Short-Term
Memory (LSTM). We developed models of run traces of malicious and benign
Portable Executable (PE) files. We created our dataset from run trace outputs
obtained from dynamic analysis of PE files. The obtained dataset was in the
instruction format as a sequence and was called Instruction as a Sequence Model
(ISM). By splitting the first dataset into basic blocks, we obtained the second
one called Basic Block as a Sequence Model (BSM). The experiments showed that
the ISM achieved an accuracy of 87.51% and a false positive rate of 18.34%,
while BSM achieved an accuracy of 99.26% and a false positive rate of 2.62%.
