Traffic flow features as metrics (TFFM): detection of application layer level DDOS attack scope of IOT traffic flows

  • Authors

    • Kalathiripi Rambabu
    • N Venkatram
    2018-03-18
    https://doi.org/10.14419/ijet.v7i2.7.10293
  • IoT, DDOS, Botnet, DDoSFlowgen, MEC Shield, Preventive Measures, Traffic Flow, Mirae.

  • Abstract

    The phenomenal and continuous growth of diversified IOT (Internet of Things) dependent networks has open for security and connectivity challenges. This is due to the nature of IOT devices, loosely coupled behavior of internetworking, and heterogenic structure of the networks.  These factors are highly vulnerable to traffic flow based DDOS (distributed-denial of services) attacks. The botnets such as “mirae†noticed in recent past exploits the IoT devises and tune them to flood the traffic flow such that the target network exhaust to response to benevolent requests. Hence the contribution of this manuscript proposed a novel learning-based model that learns from the traffic flow features defined to distinguish the DDOS attack prone traffic flows and benevolent traffic flows. The performance analysis was done empirically by using the synthesized traffic flows that are high in volume and source of attacks. The values obtained for statistical metrics are evincing the significance and robustness of the proposed model

  • References

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  • How to Cite

    Rambabu, K., & Venkatram, N. (2018). Traffic flow features as metrics (TFFM): detection of application layer level DDOS attack scope of IOT traffic flows. International Journal of Engineering & Technology, 7(2.7), 203-208. https://doi.org/10.14419/ijet.v7i2.7.10293

    Received date: 2018-03-18

    Accepted date: 2018-03-18

    Published date: 2018-03-18