Convergence Optimization of Backpropagation Artificial Neural Network Used for Dichotomous Classification of Intrusion Detection Dataset

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F17%3APU122825" target="_blank" >RIV/00216305:26230/17:PU122825 - isvavai.cz</a>

Result on the web

<a href="http://www.jcomputers.us/vol12/jcp1202-06.pdf" target="_blank" >http://www.jcomputers.us/vol12/jcp1202-06.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17706/jcp.12.2.143-155" target="_blank" >10.17706/jcp.12.2.143-155</a>

Result language

angličtina

Original language name

Convergence Optimization of Backpropagation Artificial Neural Network Used for Dichotomous Classification of Intrusion Detection Dataset

Original language description

There are distinguished two categories of intrusion detection approaches utilizing machine learning according to type of input data. The first one represents network intrusion detection techniques which consider only data captured in network traffic. The second one represents general intrusion detection techniques which intake all possible data sources including host-based features as well as network-based ones. The paper demonstrates various convergence optimization experiments of a backpropagation artificial neural network using well know NSL-KDD 1999 dataset, and thus, representing the general intrusion detection. Experiments evaluating usefulness of stratified sampling on input dataset and simulated annealing employed into the backpropagation learning algorithm are performed. Both techniques provide improvement of backpropagation's learning convergence as well as classification accuracy. After 50 training cycles, classification accuracy of 84.20% is achieved when utilizing stratified sampling and accuracy of 86.5% when both stratified sampling and simulated annealing are used. In contrast, the backpropagation by itself reaches only 76.63% accuracy. Comparing to state-of-the-art work introducing the NSL-KDD dataset, there is achieved accuracy higher about 4.5%.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Computers

ISSN

1796-203X

e-ISSN

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Volume of the periodical

12

Issue of the periodical within the volume

2

Country of publishing house

CN - CHINA

Number of pages

13

Pages from-to

143-155

UT code for WoS article

000384404900006

EID of the result in the Scopus database

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