12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019, Weihai, China

Research Article

Supervised Machine Learning based Routing Detection for Smart Meter Network

Download607 downloads
  • @INPROCEEDINGS{10.4108/eai.29-6-2019.2283068,
        author={MD Raqibull  Hasan and Yanxiao  Zhao and Guodong  Wang and Yu  Luo and Lina  Pu and Rui  Wang},
        title={Supervised Machine Learning based Routing Detection for Smart Meter Network},
        proceedings={12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019, Weihai, China},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2019},
        month={6},
        keywords={routing attack detection supervised machine learning smart meter network},
        doi={10.4108/eai.29-6-2019.2283068}
    }
    
  • MD Raqibull Hasan
    Yanxiao Zhao
    Guodong Wang
    Yu Luo
    Lina Pu
    Rui Wang
    Year: 2019
    Supervised Machine Learning based Routing Detection for Smart Meter Network
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.29-6-2019.2283068
MD Raqibull Hasan1, Yanxiao Zhao2,*, Guodong Wang3, Yu Luo4, Lina Pu5, Rui Wang6
  • 1: South Dakota School of Mines and Technology
  • 2: Virginia Commonwealth University
  • 3: Massachusetts College of Liberal Arts
  • 4: Mississippi State University
  • 5: Southern Mississippi University
  • 6: Civil Aviation University of China
*Contact email: yzhao7@vcu.edu

Abstract

It is known that the Ad hoc On-Demand Distance Vector (AODV) routing protocol for smart meter network is vulnerable to denial of service attacks (e.g., black hole attack and selective forwarding attack). In this paper, we introduce supervised machine learning to detect unknown routing attacks under AODV. There are two problems in the existing intrusion detection algorithms. The fi rst problem is that the existing intrusion detection algorithms are mainly applied to a specific and known type of routing attack, which no longer work for unknown attacks. The second one is that constant thresholds are commonly used for detection. To overcome these two problems, we introduce a supervised machine learning based detection approach. To implement supervised machine learning, three steps are involved. First, features and target estimations are selected from malicious AODV behaviors in smart meter network to generate training data sets. Second, we assign a suitable classi fier including support vector machine, k-nearest neighbors and decision trees to t the training and predicted data. Third, we update our training data to maintain a dynamic threshold. Simulations are conducted using Python3.6 to evaluate the accuracy and the time overhead of our pro- posed supervised machine learning model. The simulation results show that the decision trees algorithm assures 100% accuracy with minimum time overhead to detect routing attacks in AODV.