Convergence Optimization of Backpropagation Artificial Neural Network Used for Dichotomous Classification of Intrusion Detection Dataset
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Convergence Optimization of Backpropagation Artificial Neural Network Used for Dichotomous Classification of Intrusion Detection Dataset
Popis výsledku v původním jazyce
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%.
Název v anglickém jazyce
Convergence Optimization of Backpropagation Artificial Neural Network Used for Dichotomous Classification of Intrusion Detection Dataset
Popis výsledku anglicky
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%.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2017
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Journal of Computers
ISSN
1796-203X
e-ISSN
—
Svazek periodika
12
Číslo periodika v rámci svazku
2
Stát vydavatele periodika
CN - Čínská lidová republika
Počet stran výsledku
13
Strana od-do
143-155
Kód UT WoS článku
000384404900006
EID výsledku v databázi Scopus
—