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Related Experiment Videos

A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies.

Mahdi Rabbani1, Yongli Wang1, Reza Khoshkangini2

  • 1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

Entropy (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

289
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
289

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This summary is machine-generated.

This survey provides an overview of anomaly-based network intrusion detection systems (NIDSs), detailing their phases and machine learning techniques. It highlights current challenges and future directions for effective network anomaly detection.

Area of Science:

  • Cybersecurity
  • Computer Networks
  • Machine Learning

Background:

  • Network anomaly detection systems (NADSs) are crucial for network defense.
  • Malicious activities pose a constant threat to network systems.

Purpose of the Study:

  • To provide an exhaustive overview of anomaly-based network intrusion detection systems (NIDSs).
  • To discuss contemporary malicious activities and essential properties of intrusion detection systems.

Main Methods:

  • The survey explains key phases of NADSs: pre-processing, feature extraction, and behavior detection.
  • It comprehensively reviews machine learning approaches (supervised, unsupervised, deep, ensemble) for detection and recognition.

Main Results:

Keywords:
classifier systemsdata pre-processingdatasetmachine learningmalicious behavior detection systems

Related Experiment Videos

  • Recent machine learning techniques are discussed in detail for anomaly detection.
  • Information on benchmark datasets for training and evaluating these techniques is provided.

    • Conclusions:

    • Potential challenges and future research directions for machine learning-based NADSs are identified.
    • The study emphasizes the importance of NADSs in modern network security.