Improving Intrusion Detection Accuracy using Convolution Neural Network

Abstract

Abstract: Network Intrusion Detection has been an active area of
research with the growing incidences of cybercrimes. This has
led to continuous monitoring of network traffic, analysis, and
raise alarm if any abnormality is noticed so as to trigger
appropriate response in order to curb the possibility of an attack.
One of the approaches to deal with the network intrusion
problem is to classify the network user behavior as normal or
suspicious. Soft computing based techniques are being tried out
to classify network users with higher degree of accuracy and low
false alarm rate. In this paper, we propose a classification model
for the detection of known as well as unknown network attacks
based on artificial neural network based techniques namely,
RBFN, SOM, LVQ3, SMO, and CNN. Further, in order to
improve the performance of the classifier, Z-Score normalization
has been applied for preprocessing of data. The performance of
the model has been evaluated on the NSL-KDD dataset in terms
of Precision, Accuracy, Detection rate, F-Value, and False Alarm
rate.
Keywords: Convolution Neural Network, Learning Vector
Quantization, Normalization, Self-Organizing Map, Sequential
Minimal Optimization, and Radial Basis Function Network.