Machine Learning Based SDN-enabled Distributed Denial-of-Services Attacks Detection and Mitigation System for Internet of Things

Muhammad Aslam, Dengpan Ye, Muhammad Hanif, Muhammad Asad

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

7 Citations (Scopus)

Abstract

Advancements of Internet of Things (IoT) enhance the application spectrum of smart networking and demand intelligent security measurements against cyber-attacks. Recent integration of Software Defined Networking (SDN) in IoT environments provides better network management by decoupling of control plane from forwarding plane. An advanced SDN based network management also utilize the machine learning models to classify IoT network traffic at OpenFlow Switches with the coordination of SDN controller. In this paper, we propose a novel SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation System (SDN-DMS) which utilize SDN enabled security mechanism for IoT devices with support of machine learning algorithms to develop Distributed Denial of Services (DDoS) detection and mitigation system. SDN-DMS integrates Floodlight and Pox SDN controllers to reconfigure the OpenFlow switches in order to mitigate the detected DDoS attacks by advanced Support Vector Machine (SVM) algorithms of Lagrangian Support Vector Machine (LSVM), Finite Newton Lagrangian Support Vector Machine (NLSVM), Smooth Support Vector Machine (SSVM) and Finite Newton Support Vector Machine (NSVM). SDN-DMS measures the network behaviour of IoT devices by collecting the network traffic and classifying the traffic as normal and DDoS attacks by using an environment-specific dataset.
Original languageEnglish
Title of host publicationMachine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings
EditorsXiaofeng Chen, Hongyang Yan, Qiben Yan, Xiangliang Zhang
PublisherSpringer Nature
Pages180-194
Number of pages15
ISBN (Print)9783030622220
DOIs
Publication statusPublished - 2020
EventMachine Learning for Cyber Security, Third International Conference, ML4CS 2020 - Guangzhou, China
Duration: 08 Oct 202010 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12486 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceMachine Learning for Cyber Security, Third International Conference, ML4CS 2020
Abbreviated titleML4CS 2020
Country/TerritoryChina
CityGuangzhou
Period08 Oct 202010 Oct 2020

Keywords

  • cyber-attacks
  • internet of things
  • distributed denial of services
  • software defined networking
  • machine learning
  • detection
  • mitigation
  • Cyber-attacks
  • Internet of Things
  • Software defined networking
  • Mitigation
  • Machine learning
  • Detection
  • Distributed denial of services

Fingerprint

Dive into the research topics of 'Machine Learning Based SDN-enabled Distributed Denial-of-Services Attacks Detection and Mitigation System for Internet of Things'. Together they form a unique fingerprint.

Cite this