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Increasing the Effectiveness of Network Intrusion Detection Systems (NIDSs) by Using Multiplex Networks and Visibility Graphs

Author

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  • Sergio Iglesias Perez

    (Data, Complex Networks and Cybersecurity Sciences Technological Institute, University Rey Juan Carlos, 28028 Madrid, Spain)

  • Regino Criado

    (Data, Complex Networks and Cybersecurity Sciences Technological Institute, University Rey Juan Carlos, 28028 Madrid, Spain
    Departamento de Matemática Aplicada, Ciencia e Ingeniería de los Materiales y Tecnología Electrónica, ESCET Universidad Rey Juan Carlos, C/Tulipán s/n, 28933 Mostoles, Spain
    Center for Computational Simulation, Universidad Politécnica de Madrid, 28223 Madrid, Spain)

Abstract

In this paper, we present a new approach to NIDS deployment based on machine learning. This new approach is based on detecting attackers by analyzing the relationship between computers over time. The basic idea that we rely on is that the behaviors of attackers’ computers are different from those of other computers, because the timings and durations of their connections are different and therefore easy to detect. This approach does not analyze each network packet statistically. It analyzes, over a period of time, all traffic to obtain temporal behaviors and to determine if the IP is an attacker instead of that packet. IP behavior analysis reduces drastically the number of alerts generated. Our approach collects all interactions between computers, transforms them into time series, classifies them, and assembles them into a complex temporal behavioral network. This process results in the complex characteristics of each computer that allow us to detect which are the attackers’ addresses. To reduce the computational efforts of previous approaches, we propose to use visibility graphs instead of other time series classification methods, based on signal processing techniques. This new approach, in contrast to previous approaches, uses visibility graphs and reduces the computational time for time series classification. However, the accuracy of the model is maintained.

Suggested Citation

  • Sergio Iglesias Perez & Regino Criado, 2022. "Increasing the Effectiveness of Network Intrusion Detection Systems (NIDSs) by Using Multiplex Networks and Visibility Graphs," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
  • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:107-:d:1015691
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    References listed on IDEAS

    as
    1. Iglesias Pérez, Sergio & Moral-Rubio, Santiago & Criado, Regino, 2021. "A new approach to combine multiplex networks and time series attributes: Building intrusion detection systems (IDS) in cybersecurity," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    2. Massimiliano Zanin & David Papo & Miguel Romance & Regino Criado & Santiago Moral, 2016. "The topology of card transaction money flows," Papers 1605.04938, arXiv.org.
    3. Zanin, Massimiliano & Papo, David & Romance, Miguel & Criado, Regino & Moral, Santiago, 2016. "The topology of card transaction money flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 134-140.
    4. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    5. Massimiliano Zanin & Miguel Romance & Santiago Moral & Regino Criado, 2018. "Credit Card Fraud Detection through Parenclitic Network Analysis," Complexity, Hindawi, vol. 2018, pages 1-9, May.
    Full references (including those not matched with items on IDEAS)

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