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Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for

Muhammad Aslam1, Dengpan Ye2, Aqil Tariq3

  • 1School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Glasgow G72 0LH, UK.

Sensors (Basel, Switzerland)
|April 12, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework. It enhances IoT security by accurately detecting and mitigating DDoS attacks with improved performance over existing solutions.

Keywords:
Distributed Denial-of-ServicesInternet of Thingsadaptive machine learningdetectionmitigationnetwork securitysoftware defined networking

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • Internet of Things (IoT) infrastructure faces significant Distributed Denial-of-Services (DDoS) threats.
  • Existing enterprise network security solutions are costly and not scalable for IoT environments.
  • Software-Defined Networking (SDN) integration offers reduced overhead and enhanced security for IoT.

Purpose of the Study:

  • To propose an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework.
  • To develop a scalable and accurate security mechanism for IoT devices against DDoS attacks.
  • To improve upon the low accuracy and detection rates of current sampling-based security approaches in SDN-enabled IoT.

Main Methods:

  • Implemented an adaptive, multi-layered feed-forwarding machine learning scheme for DDoS detection.
  • Utilized Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbor (kNN), and Logistic Regression (LR) classifiers in the first layer.
  • Employed an Ensemble Voting (EV) algorithm in the second layer and real-time traffic analysis in the third layer for detection and mitigation via SDN controller and Open Flow (OF) switches.

Main Results:

  • The proposed AMLSDM framework demonstrated higher accuracy in DDoS attack detection.
  • The framework achieved a significantly lower false alarm rate compared to existing state-of-the-art solutions.
  • Experimental results validated the enhanced performance and effectiveness of the AMLSDM framework.

Conclusions:

  • The AMLSDM framework provides an effective and scalable solution for DDoS attack detection and mitigation in SDN-enabled IoT networks.
  • Adaptive machine learning integration significantly improves the accuracy and reduces false alarms in identifying sophisticated cyber threats.
  • The proposed approach offers a promising direction for securing the rapidly expanding landscape of IoT infrastructure.