Title: A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data,
Abstract: Gated Recurrent Unit (GRU) is a recently-developed variation of the long
short-term memory (LSTM) unit, both of which are types of recurrent neural
network (RNN). Through empirical evidence, both models have been proven to be
effective in a wide variety of machine learning tasks such as natural language
processing (Wen et al., 2015), speech recognition (Chorowski et al., 2015), and
text classification (Yang et al., 2016). Conventionally, like most neural
networks, both of the aforementioned RNN variants employ the Softmax function
as its final output layer for its prediction, and the cross-entropy function
for computing its loss. In this paper, we present an amendment to this norm by
introducing linear support vector machine (SVM) as the replacement for Softmax
in the final output layer of a GRU model. Furthermore, the cross-entropy
function shall be replaced with a margin-based function. While there have been
similar studies (Alalshekmubarak & Smith, 2013; Tang, 2013), this proposal is
primarily intended for binary classification on intrusion detection using the
2013 network traffic data from the honeypot systems of Kyoto University.
Results show that the GRU-SVM model performs relatively higher than the
conventional GRU-Softmax model. The proposed model reached a training accuracy
of ~81.54% and a testing accuracy of ~84.15%, while the latter was able to
reach a training accuracy of ~63.07% and a testing accuracy of ~70.75%. In
addition, the juxtaposition of these two final output layers indicate that the
SVM would outperform Softmax in prediction time - a theoretical implication
which was supported by the actual training and testing time in the study.