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Adapting New Data In Intrusion Detection Systems
Aslihan Akyol, Bekir KARLIK, Bariş Koçer
Pages - 1 - 11     |    Revised - 31-01-2019     |    Published - 28-02-2019
Volume - 8   Issue - 1    |    Publication Date - February 2019  Table of Contents
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KEYWORDS
Intrusion Detection Systems, Transfer Learning, Genetic Transfer Learning, Genetic Algorithms, Artificial Neural Networks.
ABSTRACT
Most of the introduced anomaly intrusion detection system (IDS) methods focus on achieving better detection rates and lower false alarm rates. However, when it comes to real-time applications many additional issues come into the picture. One of them is the training datasets that are continuously becoming outdated. It is vital to use an up-to-date dataset while training the system. But the trained system will become insufficient if network behaviors change. As well known, frequent alteration is in the nature of computer networks. On the other hand it is costly to continually collect and label datasets while frequently training the system from scratch and discarding old knowledge is a waste. To overcome this problem, we propose the use of transfer learning which benefits from the previous gained knowledge. The carried out experiments stated that transfer learning helps to utilize previously obtained knowledge, improves the detection rate and reduces the need to recollect the whole dataset.
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Dr. Aslihan Akyol
Independent Researcher - Turkey
aslihan.ozkaya@gmail.com
Professor Bekir KARLIK
McGill University, Neurosurgical Simulation Research & Training Centre, Montréal, QC - Canada
Dr. Bariş Koçer
Selcuk University, Department of Computer Engineering, Konya, Turkey - Turkey