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Big Data helps SDN to detect intrusions and secure data flows

Big Data helps SDN to detect intrusions and secure data flows

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In this chapter, we examine the security risks of SDN with the consideration of intrusions and abnormal data flows. Specifically, we discuss how SDN brings unique risks and threats to network service providers and customers. Then, we discuss the potential of integrating Big Data analytics into SDN for security enhancement and provide some examples to end this chapter.

Chapter Contents:

  • 17.1 Introduction
  • 17.2 Security issues of SDN
  • 17.2.1 Security issues in control channel
  • 17.2.2 Denial-of-service (DoS) attacks
  • 17.2.2.1 Unauthenticated channel DoS (outsider)
  • 17.2.2.2 Man-in-the-middle DoS (outsider)
  • 17.2.2.3 Compromised host DoS (insider)
  • 17.2.3 Simulation of control channel attack on SDN
  • 17.3 Big Data techniques for security threats in SDN
  • 17.3.1 Big Data analytics
  • 17.3.2 Data analytics for threat detection
  • 17.4 QoS consideration in SDN with security services
  • 17.4.1 Delay guarantee for security traversal
  • 17.4.1.1 Optimal security routing
  • 17.4.1.2 Optimal security traversal with middlebox addition
  • 17.4.2 Traffic load balancing
  • 17.4.2.1 Traffic-aware load balancing using SDN
  • 17.4.2.2 Delay performance comparison
  • 17.5 Big Data applications for securing SDN
  • 17.5.1 Packet inspection
  • 17.6 Open issues and challenge
  • 17.7 Summary and conclusion
  • References

Inspec keywords: Big Data; software defined networking; data analysis; computer network security

Other keywords: abnormal data flows; SDN; network service providers; security risks; Big Data analytics; secure data flows; security enhancement; intrusion detection

Subjects: Data security; Computer communications; Computer networks and techniques

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