IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i24p4768-d297800.html
   My bibliography  Save this article

Method of Early Detection of Cyber-Attacks on Telecommunication Networks Based on Traffic Analysis by Extreme Filtering

Author

Listed:
  • Andrey Privalov

    (Emperor Alexander I Saint-Petersburg State Transport University, 9 Moskovsky pr., 190031 St. Petersburg, Russia)

  • Vera Lukicheva

    (Emperor Alexander I Saint-Petersburg State Transport University, 9 Moskovsky pr., 190031 St. Petersburg, Russia)

  • Igor Kotenko

    (Saint-Petersburg Institute for Informatics and Automation of Russian Academy of Sciences (SPIIRAS), 39, 14 Liniya, 199178 St. Petersburg, Russia)

  • Igor Saenko

    (Saint-Petersburg Institute for Informatics and Automation of Russian Academy of Sciences (SPIIRAS), 39, 14 Liniya, 199178 St. Petersburg, Russia)

Abstract

The paper suggests a method of early detection of cyber-attacks by using DDoS attacks as an example) using the method of extreme filtering in a mode close real time. The process of decomposition of the total signal (additive superposition of attacking and legitimate effects) and its decomposition using the method of extreme filtering is simulated. A profile model of a stochastic network is proposed. This allows to specify the influence of the intruder on the network using probabilistic-time characteristics. Experimental evaluation of metrics characterizing the cyber-attack is given. It is demonstrated how obtained values of metrics confirm the process of attack preparation, for instance the large-scaled telecommunication network, which includes the proposed method for early detection of attacks, has a recovery time of no more than 9 s, and the parameters of quality of service remain in an acceptable range.

Suggested Citation

  • Andrey Privalov & Vera Lukicheva & Igor Kotenko & Igor Saenko, 2019. "Method of Early Detection of Cyber-Attacks on Telecommunication Networks Based on Traffic Analysis by Extreme Filtering," Energies, MDPI, vol. 12(24), pages 1-14, December.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4768-:d:297800
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/24/4768/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/24/4768/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dai Wang & Xiaohong Guan & Ting Liu & Yun Gu & Chao Shen & Zhanbo Xu, 2014. "Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids," Energies, MDPI, vol. 7(3), pages 1-22, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Igor Kotenko & Igor Saenko & Oleg Lauta & Aleksander Kribel, 2020. "An Approach to Detecting Cyber Attacks against Smart Power Grids Based on the Analysis of Network Traffic Self-Similarity," Energies, MDPI, vol. 13(19), pages 1-24, September.
    2. Andrey Privalov & Vera Lukicheva & Igor Kotenko & Igor Saenko, 2020. "Increasing the Sensitivity of the Method of Early Detection of Cyber-Attacks in Telecommunication Networks Based on Traffic Analysis by Extreme Filtering," Energies, MDPI, vol. 13(11), pages 1-18, June.
    3. Mazhar Javed Awan & Umar Farooq & Hafiz Muhammad Aqeel Babar & Awais Yasin & Haitham Nobanee & Muzammil Hussain & Owais Hakeem & Azlan Mohd Zain, 2021. "Real-Time DDoS Attack Detection System Using Big Data Approach," Sustainability, MDPI, vol. 13(19), pages 1-19, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giacomo Valente & Vittoriano Muttillo & Mirco Muttillo & Gianluca Barile & Alfiero Leoni & Walter Tiberti & Luigi Pomante, 2019. "SPOF—Slave Powerlink on FPGA for Smart Sensors and Actuators Interfacing for Industry 4.0 Applications," Energies, MDPI, vol. 12(9), pages 1-13, April.
    2. Mihai Sanduleac & Gianluca Lipari & Antonello Monti & Artemis Voulkidis & Gianluca Zanetto & Antonello Corsi & Lucian Toma & Giampaolo Fiorentino & Dumitru Federenciuc, 2017. "Next Generation Real-Time Smart Meters for ICT Based Assessment of Grid Data Inconsistencies," Energies, MDPI, vol. 10(7), pages 1-16, June.
    3. Daniel Sousa-Dias & Daniel Amyot & Ashkan Rahimi-Kian & John Mylopoulos, 2023. "A Review of Cybersecurity Concerns for Transactive Energy Markets," Energies, MDPI, vol. 16(13), pages 1-32, June.
    4. Zhengwei Qu & Jingchuan Yang & Yansheng Lang & Yunjing Wang & Xiaoming Han & Xinyue Guo, 2022. "Earth-Mover-Distance-Based Detection of False Data Injection Attacks in Smart Grids," Energies, MDPI, vol. 15(5), pages 1-16, February.
    5. Reda, Haftu Tasew & Anwar, Adnan & Mahmood, Abdun, 2022. "Comprehensive survey and taxonomies of false data injection attacks in smart grids: attack models, targets, and impacts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    6. Yazhou Jiang & Chen-Ching Liu & Yin Xu, 2016. "Smart Distribution Systems," Energies, MDPI, vol. 9(4), pages 1-20, April.
    7. Xuan Liu & Xingdong Liu & Zuyi Li, 2015. "Cyber Risk Assessment of Transmission Lines in Smart Grids," Energies, MDPI, vol. 8(12), pages 1-15, December.
    8. Derya Betul Unsal & Taha Selim Ustun & S. M. Suhail Hussain & Ahmet Onen, 2021. "Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation," Energies, MDPI, vol. 14(9), pages 1-36, May.
    9. David Macii & Daniele Fontanelli & Grazia Barchi, 2020. "A Distribution System State Estimator Based on an Extended Kalman Filter Enhanced with a Prior Evaluation of Power Injections at Unmonitored Buses," Energies, MDPI, vol. 13(22), pages 1-25, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4768-:d:297800. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.