A Clustering Approach for Intrusion Detection with Big Data Processing on Parallel Computing Platform

Year 2019, Volume: 7 Issue: 3, 286 - 293, 30.07.2019

Ozgur Koray Sahingoz

https://doi.org/10.17694/bajece.563167

Cited By: 4

Abstract

Abstract— In
recent years there is a growing number of attacks in the computer networks.
Therefore, the use of a prevention mechanism is an inevitable need for security
admins. Although firewalls are preferred as the first layer of protection, it
is not sufficient for preventing lots of the attacks, especially from the
insider attacks. Intrusion Detection Systems (IDSs) have emerged as an
effective solution to these types of attacks. For increasing the efficiency of the
IDS system, a dynamic solution, which can adapt itself and can detect new types
of intrusions with a dynamic structure by the use of learning algorithms is mostly
preferred. In previous years, some machine learning approaches are implemented
in lots of IDSs. In the current position of artificial intelligence, most of
the learning systems are transferred with the use of Deep Learning approaches
due to its flexibility and the use of Big Data with high accuracy. In this
paper, we propose a clustered approach to detect the intrusions in a network.
Firstly, the system is trained with Deep Neural Network on a Big Data set by
accelerating its performance with the use of CUDA architecture. Experimental
results show that the proposed system has a very good accuracy rate and low
runtime duration with the use of this parallel computation architecture. Additionally,
the proposed system needs a relatively small duration for training the system 

Keywords

Deep Neural Network, Big Data, CUDA, GPU, Parallel Computation

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