This paper proposes a deep recurrent neural network (RNN)-based traffic
classification scheme (deep RNN-TCS) for classifying applications from
traffic patterns in a hybrid edge computing and cloud computing
architecture.We can also classify traffic from a cloud server, but there
will be a time delay when packets transfer to the server. Therefore, the
traffic classification is possible almost in realtime when it performed on
edge computing nodes. However, training takes a lot of time and needs a lot
of computing resources to learn traffic patterns. Therefore, it is efficient
to perform training on cloud server and to perform serving on edge computing
node. Here, a cloud server collects and stores output labels corresponding
to the application packets. Then, it trains those data and generates
inferred functions. An edge computation node receives the inferred functions
and executes classification. Compared to deep packet inspection (DPI), which
requires the periodic verification of existing signatures and updated
application information (e.g., versions adding new features), the proposed
scheme can classify the applications in an automated manner. Also, deep
learning can automatically make classifiers for traffic classification when
there is enough data. Specifically, input features and output labels are
defined for classification as traffic packets and target applications,
respectively, which are created as two-dimensional images. As our training
data, traffic packets measured at Universitat Politecnica de Catalunya
Barcelonatech were utilized. Accordingly, the proposed deep RNN-TCS is
implemented using a deep long short-term memory system. Through extensive
simulation-based experiments, it is verified that the proposed deep RNN-TCS
achieves almost 5% improvement in accuracy (96% accuracy) while operating
500 times faster (elapsed time) compared to the conventional scheme.