Co-FQL

Anomaly detection using cooperative fuzzy Q-learning in network

Shahaboddin Shamshirband, Babak Daghighi, Nor Badrul Anuar, Miss Laiha Mat Kiah, Ahmed Patel, Ajith Abraham

    Research output: Contribution to journalArticle

    5 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)1345-1357
    Number of pages13
    JournalJournal of Intelligent and Fuzzy Systems
    Volume28
    Issue number3
    DOIs
    Publication statusPublished - 2015

    Fingerprint

    Q-learning
    Anomaly Detection
    Attack
    Denial of Service
    Learning algorithms
    Intrusion detection
    Learning Algorithm
    Mobile devices
    Fuzzy logic
    Computational complexity
    Wireless networks
    Classifiers
    Decision making
    Adaptive Learning
    Cooperative Systems
    Fuzzy Logic Controller
    Flooding
    Controllers
    Countermeasures
    Intrusion Detection

    Keywords

    • cooperative IDS
    • fuzzy system
    • Intrusion detection
    • multi agent system
    • reinforcement learning

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Engineering(all)
    • Statistics and Probability

    Cite this

    Shamshirband, S., Daghighi, B., Anuar, N. B., Kiah, M. L. M., Patel, A., & Abraham, A. (2015). Co-FQL: Anomaly detection using cooperative fuzzy Q-learning in network. Journal of Intelligent and Fuzzy Systems, 28(3), 1345-1357. https://doi.org/10.3233/IFS-141419

    Co-FQL : Anomaly detection using cooperative fuzzy Q-learning in network. / Shamshirband, Shahaboddin; Daghighi, Babak; Anuar, Nor Badrul; Kiah, Miss Laiha Mat; Patel, Ahmed; Abraham, Ajith.

    In: Journal of Intelligent and Fuzzy Systems, Vol. 28, No. 3, 2015, p. 1345-1357.

    Research output: Contribution to journalArticle

    Shamshirband, S, Daghighi, B, Anuar, NB, Kiah, MLM, Patel, A & Abraham, A 2015, 'Co-FQL: Anomaly detection using cooperative fuzzy Q-learning in network', Journal of Intelligent and Fuzzy Systems, vol. 28, no. 3, pp. 1345-1357. https://doi.org/10.3233/IFS-141419
    Shamshirband, Shahaboddin ; Daghighi, Babak ; Anuar, Nor Badrul ; Kiah, Miss Laiha Mat ; Patel, Ahmed ; Abraham, Ajith. / Co-FQL : Anomaly detection using cooperative fuzzy Q-learning in network. In: Journal of Intelligent and Fuzzy Systems. 2015 ; Vol. 28, No. 3. pp. 1345-1357.
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