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Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey.

Sehar Zehra1,2, Ummay Faseeha1,3, Hassan Jamil Syed1,4,5

  • 1FAST School of Computing, National University of Computer and Emerging Sciences, Karachi 75030, Pakistan.

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Summary
This summary is machine-generated.

Network Function Virtualization (NFV) security in IoT and sensor networks is enhanced by anomaly detection. This survey identifies machine learning algorithms to effectively mitigate cyber attacks and safeguard network integrity.

Keywords:
Internet of Things (IoT)anomaly detectioncyber-attacksmachine learning basednetwork function virtualization (NFV)security challengessupervised learningunsupervised learning

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • Network Function Virtualization (NFV) offers significant benefits like cost reduction and flexibility.
  • NFV is critical for optimizing sensor and Internet of Things (IoT) networks.
  • NFV adoption introduces complex security challenges.

Purpose of the Study:

  • To explore security challenges in NFV, particularly within IoT and sensor networks.
  • To propose anomaly detection techniques for mitigating cyber threats in NFV environments.
  • To evaluate machine learning algorithms for effective anomaly detection in NFV networks.

Main Methods:

  • Survey of existing literature on NFV security.
  • Analysis of anomaly detection techniques, focusing on machine learning.
  • Evaluation of various ML algorithms for network-based anomaly detection in NFV.

Main Results:

  • Identified key security vulnerabilities in NFV deployments.
  • Assessed the efficacy of different machine learning algorithms for anomaly detection.
  • Highlighted the strengths and weaknesses of evaluated algorithms.

Conclusions:

  • Anomaly detection, particularly using machine learning, is crucial for securing NFV.
  • Provides insights into the most efficient algorithms for timely threat mitigation.
  • Aims to enhance NFV security for robust sensor and IoT systems.