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A comprehensive survey on network anomaly detection

Author

Listed:
  • Gilberto Fernandes

    (Universidade da Beira Interior)

  • Joel J. P. C. Rodrigues

    (Universidade da Beira Interior
    National Institute of Telecommunications (Inatel)
    ITMO University
    University of Fortaleza (UNIFOR))

  • Luiz Fernando Carvalho

    (State University of Campinas (UNICAMP))

  • Jalal F. Al-Muhtadi

    (King Saud University (KSU))

  • Mario Lemes Proença

    (State University of Londrina (UEL))

Abstract

Nowadays, there is a huge and growing concern about security in information and communication technology among the scientific community because any attack or anomaly in the network can greatly affect many domains such as national security, private data storage, social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad research area, and many different techniques and approaches for this purpose have emerged through the years. In this study, the main objective is to review the most important aspects pertaining to anomaly detection, covering an overview of a background analysis as well as a core study on the most relevant techniques, methods, and systems within the area. Therefore, in order to ease the understanding of this survey’s structure, the anomaly detection domain was reviewed under five dimensions: (1) network traffic anomalies, (2) network data types, (3) intrusion detection systems categories, (4) detection methods and systems, and (5) open issues. The paper concludes with an open issues summary discussing presently unsolved problems, and final remarks.

Suggested Citation

  • Gilberto Fernandes & Joel J. P. C. Rodrigues & Luiz Fernando Carvalho & Jalal F. Al-Muhtadi & Mario Lemes Proença, 2019. "A comprehensive survey on network anomaly detection," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 70(3), pages 447-489, March.
    • Handle: RePEc:spr:telsys:v:70:y:2019:i:3:d:10.1007_s11235-018-0475-8
    DOI: 10.1007/s11235-018-0475-8
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    References listed on IDEAS

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    1. Pan, Junshan & Hu, Hanping & Liu, Ying, 2014. "Human behavior during Flash Crowd in web surfing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 212-219.
    2. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    3. Osman Salem & Alexey Guerassimov & Ahmed Mehaoua & Anthony Marcus & Borko Furht, 2014. "Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 5(1), pages 20-45, January.
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    Cited by:

    1. Tomás Sureda Riera & Juan-Ramón Bermejo Higuera & Javier Bermejo Higuera & José-Javier Martínez Herraiz & Juan-Antonio Sicilia Montalvo, 2020. "Prevention and Fighting against Web Attacks through Anomaly Detection Technology. A Systematic Review," Sustainability, MDPI, vol. 12(12), pages 1-45, June.
    2. Carlos Paula Lemos & Antônio Cláudio Paschoarelli Veiga & Sandro Adriano Fasolo, 2021. "Estimation of $$\alpha -\kappa -\mu $$ α - κ - μ mobile fading channel parameters using evolutionary algorithms," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 77(1), pages 189-211, May.
    3. Xiaoping Li & Ching-Chun Chang & Yanjun Liu, 2021. "A generalized Chinese remainder theorem-based proactive multi-secret sharing scheme for global wide area network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 78(1), pages 49-56, September.
    4. Mohammad Masdari & Hemn Khezri, 2021. "Towards fuzzy anomaly detection-based security: a comprehensive review," Fuzzy Optimization and Decision Making, Springer, vol. 20(1), pages 1-49, March.

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