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Mobile Networks and Applications

, Volume 24, Issue 6, pp 1896–1923 | Cite as

SDNFV Based Threat Monitoring and Security Framework for Multi-Access Edge Computing Infrastructure

  • Prabhakar KrishnanEmail author
  • Subhasri Duttagupta
  • Krishnashree Achuthan
Article
  • 73 Downloads

Abstract

DDoS botnet attacks such as Advanced Persistent & Ransom DoS assaults, Botnets and Application DDoS flood attacks are examples of multi-vector, sophisticated application-layer attacks. Conventional IT security approaches are centralized and have limitations in terms of scale, network-wide monitoring and resources for distributed detection. This paper proposes a newer approach that integrates multi-layer cooperative security intelligence on to a converged Software-Defined-Networking/Network-Function-Virtualization architecture in typical Multi-access Edge Computing (MEC) scenario. The key features of framework include: a) distributed lightweight real-time DDoS Threat Analytics and Response Framework (DTARS), to identify DDoS/botnets closer to the source of attacks b) behavioral monitoring and profiling functions in data plane and validation of control plane operations, c) advanced correlation, signature, and anomaly detection techniques, d) real-time threat analytics system e) scalable and agile mitigation mechanisms based on a stateful-data plane and security-aware SDN stack. We evaluate the performance of DTARS framework within three practical MEC case studies: SDN enabled Mobile LTE MEC network, SDN enabled IoT MEC network and Software-Defined Datacenter Edge network. In comparison to legacy MEC network, DTARS incurs about 60% less overhead than the Legacy LTE and 40% lesser than a prior OVS SDN based MEC-LTE solution, detection speed that was about 10x faster, detection accuracy of about 96% at different attack intensities and improves the overall end-to-end connection management performance under rapid scaling of end users.

Keywords

MEC LTE SDN NFV SDNFV OpenFlow IoT Cloud Edge networks DDoS Botnet Network Security Threat Analytics Security Network Intrusion Detection system NIDS 

Notes

Acknowledgments

This research was supported by the office of Dean-Research at Amrita Vishwa Vidyapeetham, Amritapuri campus, India and the Visveswaraya Ph.D. fellowship from the Government of India.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Prabhakar Krishnan
    • 1
    Email author
  • Subhasri Duttagupta
    • 2
  • Krishnashree Achuthan
    • 1
  1. 1.Center for Cybersecurity Systems and NetworksKollamIndia
  2. 2.Department of Computer Science and EngineeringAmrita Vishwa VidyapeethamKollamIndia

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