A NEURAL-VISUALIZATION IDS FOR HONEYNET DATA

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F12%3A86084429" target="_blank" >RIV/61989100:27740/12:86084429 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1142/S0129065712500050" target="_blank" >http://dx.doi.org/10.1142/S0129065712500050</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S0129065712500050" target="_blank" >10.1142/S0129065712500050</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A NEURAL-VISUALIZATION IDS FOR HONEYNET DATA

Popis výsledku v původním jazyce

Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspectionof the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and co

Název v anglickém jazyce

A NEURAL-VISUALIZATION IDS FOR HONEYNET DATA

Popis výsledku anglicky

Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspectionof the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and co

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

International Journal of Neural Systems

ISSN

0129-0657

e-ISSN

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Svazek periodika

22

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

18

Strana od-do

1-18

Kód UT WoS článku

000302210200005

EID výsledku v databázi Scopus

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