Co-FQL: Anomaly detection using cooperative fuzzy Q-learning in network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86099389" target="_blank" >RIV/61989100:27240/15:86099389 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/15:86099389

Výsledek na webu

<a href="http://dx.doi.org/10.3233/IFS-141419" target="_blank" >http://dx.doi.org/10.3233/IFS-141419</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/IFS-141419" target="_blank" >10.3233/IFS-141419</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Co-FQL: Anomaly detection using cooperative fuzzy Q-learning in network

Popis výsledku v původním jazyce

Wireless networks are increasingly overwhelmed by Distributed Denial of Service (DDoS) attacks by generating flooding packets that exhaust critical computing and communication resources of a victim's mobile device within a very short period of time. This must be protected. Effective detection of DDoS attacks requires an adaptive learning classifier, with less computational complexity, and an accurate decision making to stunt such attacks. We propose a distributed intrusion detection system called Cooperative IDS to protect wireless nodes within the network and target nodes from DDoS attacks by using a Cooperative Fuzzy Q-learning (Co-FQL) optimization algorithmic technique to identify the attack patterns and take appropriate countermeasures. The Co-FQL algorithm was trained and tested to establish its performance by generating attacks from the NSL-KDD and "CAIDA DDoS Attack 2007" datasets during the simulation experiments. Experimental results show that the proposed Co-FQL IDS has a 90.58% higher accuracy of detection rate than Fuzzy Logic Controller or Q-learning algorithm or Fuzzy Q-learning alone. (C) 2015 - IOS Press and the authors. All rights reserved.

Název v anglickém jazyce

Co-FQL: Anomaly detection using cooperative fuzzy Q-learning in network

Popis výsledku anglicky

Wireless networks are increasingly overwhelmed by Distributed Denial of Service (DDoS) attacks by generating flooding packets that exhaust critical computing and communication resources of a victim's mobile device within a very short period of time. This must be protected. Effective detection of DDoS attacks requires an adaptive learning classifier, with less computational complexity, and an accurate decision making to stunt such attacks. We propose a distributed intrusion detection system called Cooperative IDS to protect wireless nodes within the network and target nodes from DDoS attacks by using a Cooperative Fuzzy Q-learning (Co-FQL) optimization algorithmic technique to identify the attack patterns and take appropriate countermeasures. The Co-FQL algorithm was trained and tested to establish its performance by generating attacks from the NSL-KDD and "CAIDA DDoS Attack 2007" datasets during the simulation experiments. Experimental results show that the proposed Co-FQL IDS has a 90.58% higher accuracy of detection rate than Fuzzy Logic Controller or Q-learning algorithm or Fuzzy Q-learning alone. (C) 2015 - IOS Press and the authors. All rights reserved.

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Journal of Intelligent and Fuzzy Systems

ISSN

1064-1246

e-ISSN

—

Svazek periodika

3

Číslo periodika v rámci svazku

28

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

1345-1357

Kód UT WoS článku

000349834500034

EID výsledku v databázi Scopus

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