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Detection and Classification of Malicious Flows in Software-Defined Networks Using Data Mining Techniques.

Marek Amanowicz1, Damian Jankowski2

  • 1NASK National Research Institute, 01-045 Warsaw, Poland.

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|April 30, 2021
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Summary
This summary is machine-generated.

This study introduces a novel system for securing software-defined networks (SDNs) using data mining to detect malicious network flows. The proposed method effectively identifies and classifies threats, enhancing SDN security against intruders.

Keywords:
MininetOpenDaylightdata miningflow classificationflow featuressoftware-defined network

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Area of Science:

  • Computer Science
  • Network Security
  • Data Mining

Background:

  • The proliferation of mobile devices and cloud computing drives the adoption of Software-Defined Networks (SDNs).
  • SDNs offer enhanced network security through software abstraction between control and data planes.
  • Existing security measures in SDNs require improvement to counter evolving threats.

Purpose of the Study:

  • To propose a comprehensive system for securing SDNs against malicious activities.
  • To leverage SDN's native features and data mining for intrusion detection and classification.
  • To enhance the security posture of both wired and wireless SDN environments.

Main Methods:

  • Developed a system integrating SDN features with data mining algorithms.
  • Focused on mechanisms for flow rule generation and intelligent flow classification.
  • Validated the system within an SDN testbed simulating realistic network traffic.

Main Results:

  • The system successfully detected and classified malicious flows in the SDN data plane.
  • Experimental results demonstrated effective mitigation of threats from intruder activities.
  • The combined data mining techniques outperformed existing solutions in detection and classification accuracy.

Conclusions:

  • The proposed system offers a robust solution for enhancing SDN security.
  • Data mining is a valuable tool for identifying and mitigating network threats in SDNs.
  • The approach is effective in real-world SDN testbed environments.