1 research outputs found
Comparison of System Call Representations for Intrusion Detection
Over the years, artificial neural networks have been applied successfully in
many areas including IT security. Yet, neural networks can only process
continuous input data. This is particularly challenging for security-related
non-continuous data like system calls. This work focuses on four different
options to preprocess sequences of system calls so that they can be processed
by neural networks. These input options are based on one-hot encoding and
learning word2vec or GloVe representations of system calls. As an additional
option, we analyze if the mapping of system calls to their respective kernel
modules is an adequate generalization step for (a) replacing system calls or
(b) enhancing system call data with additional information regarding their
context. However, when performing such preprocessing steps it is important to
ensure that no relevant information is lost during the process. The overall
objective of system call based intrusion detection is to categorize sequences
of system calls as benign or malicious behavior. Therefore, this scenario is
used to evaluate the different input options as a classification task. The
results show, that each of the four different methods is a valid option when
preprocessing input data, but the use of kernel modules only is not recommended
because too much information is being lost during the mapping process.Comment: 12 pages, 1 figure, submitted to CISIS 201