Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace

Research Article

Dynamic Machine Learning Algorithm for AODV Routing Attacks Detection

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  • @INPROCEEDINGS{10.4108/eai.27-8-2020.2297866,
        author={Md Raqibull Hasan and Yanxiao  Zhao and Guodong  Wang and Yu  Luo and Lina  Pu},
        title={Dynamic Machine Learning Algorithm for AODV Routing Attacks Detection},
        proceedings={Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2020},
        month={11},
        keywords={aodv  routing  protocol routing  attack smart  meter  network dynamic machine learning},
        doi={10.4108/eai.27-8-2020.2297866}
    }
    
  • Md Raqibull Hasan
    Yanxiao Zhao
    Guodong Wang
    Yu Luo
    Lina Pu
    Year: 2020
    Dynamic Machine Learning Algorithm for AODV Routing Attacks Detection
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.27-8-2020.2297866
Md Raqibull Hasan1, Yanxiao Zhao2,*, Guodong Wang3, Yu Luo4, Lina Pu5
  • 1: Contract Professionals Incorporation, Waterford Twp, MI 48329
  • 2: ECE Department, Virginia Commonwealth University, Richmond, VA 23284
  • 3: CS Department, Massachusetts College of Liberal Arts, North Adams, MA 01247
  • 4: ECE Department, Mississippi State University, Mississippi State, MS 39762
  • 5: School of CSCE, University of Southern Mississippi, Hattiesburg, MS 39406
*Contact email: yzhao7@vcu.edu

Abstract

Ad hoc On-Demand Distance Vector (AODV) routing protocol is vulnerable to some routing attacks including blackhole attack and flooding attack. Typically, these two types of routing attacks are linked with two major malicious behaviors: fake Route Replies (RREP) and fake Route Request (RREQ) flooding. In this paper, we develop a novel dynamic machine learning approach to detect blackhole and flooding attacks in AODV. The proposed solution primarily determines three distinct features by analyzing Hello Packet, RREQ and RREP in AODV routing protocol. Then, these features are used to develop a mathematical model for dynamic learning algorithm. After that, we generate the training set of data and assign a threshold for our machine learning model using these features. This training set of data is only valid for N time slots, which is regarded as one iteration. In the following iterations, it will update the latest valid outcomes from the dynamic learning model and determine a updated threshold for the model, which significantly increases the detection accuracy. Extensive simulations have been conducted to evaluate the accuracy and the time overhead of three classifiers, e.g., support vector machine, k-nearest neighbors and decision trees. The simulation results show that the proposed algorithm can achieve very high accuracy with minimum time overhead to detect malicious behavior in AODV routing protocol.