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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