A Hybrid Convolution Neural Network with Binary Particle Swarm Optimization for Intrusion Detection

Abstract—Many companies start using could systems and
services to increase their productivity and decrease the cost
by migrating their applications, infrastructures, and data to
external cloud platforms. Using cloud systems leads to raise
the number of attacks on such systems. Protecting these cloud
platforms from different attacks becomes an essential task using
Intrusion detection systems (IDS). In general, IDS is used to
detect normal or abnormal network traffic packets. In this paper,
we proposed a hybrid intelligent IDS system based on a onedimensional
Convolution Neural Network (1D-CNN) and Binary
Particle swarm Optimization (BPSO). BPSO is employed as a
wrapper feature selection to determine the most valuable features
and reduce the high dimensionality of collected data. While 1DCNN
is employed as a binary classifier. We adopted a real dataset
called UNSW-NB15 to evaluate the proposed hybrid IDS. The
obtained results show the proposed system can detect normal
and abnormal packets with an accuracy equals 94:3%.