Control Systems and Computers, N4, 2022, Article 2

https://doi.org/10.15407/csc.2022.04.013

Control Systems and Computers, 2022, Issue 4 (300), pp. 13-23

UDC 004.05

V.O. HORBATYUK, PhD student, Institute of Cybernetics named after V.M. Hlushkova NAS of Ukraine, 03187, Kyiv, ave. Akademika Hlushkova, 40, Ukraine, ORCID: https://orcid.org/0000-0001-7544-0260, viktor.gorbatiuk@gmail.com

S.O. HORBATYUK, junior researcher, department of the theory of digital automata, Institute of Cybernetics named after V.M. Hlushkova NAS of Ukraine, 03187, Kyiv, ave. Akademika Hlushkova, 40, Ukraine, ORCID: https://orcid.org/0000-0001-6834-4211, gorbatiuk_sergiy@i.ua

Methods for Сhecking the Resistance to http Attacks on a
Smart Home by Algebraic Comparison

Cyber ​​attacks become possible because of vulnerabilities in the IT infrastructure or in a particular system. It is impossible to create the completely secure environment, but it is possible to give sufficient attention to vulnerabilities and reduce the consequences of any attacks that will exploit these vulnerabilities. It is necessary to assume the probability of an attack and be ready to take actions now to prevent them from being successful again. Time is a definite factor in mitigating the damage from a cyber security breach. Thus, the key role is laid on detecting an intrusion as soon as possible and being able to neutralize or isolate the intruder. This work aims to show common types of cyberattacks on smart homes, as well as detections and methods for their tools. in this way, the method of mathematical comparison works in the work, which allows at the stage of system design to identify the possibilities of vulnerability and, as a result, create stable web applications and services, and at the stage of operation to assess the probability of attacks on the system and predict the consequences.

Download full text! (In Ukrainian)

Keywords: cyber security, HTTP protocol, cyber attack, smart home, attack resistance, algebraic modeling, algebraic matching, formalization, security properties.

  1. 3 Types of Network Attacks to Watch Out For. [online]. Available at: <https://www.tripwire.com/state-of-security/vulnerability-management/3-types-of-network-attacks/> [Accessed: 23 Sept. 2022].
  2. What is a Network Attack? [online]. Available at: <https://www.forcepoint.com/cyber-edu/network-attack/> [Accessed: 23 Apr. 2022].
  3. Ultra fast automated DDoS detection & mitigation. [online]. Available at: <https://anuragbhatia.com/2017/10/networking/isp-column/ultra-fast-automated-ddos-detection-mitigation/> [Accessed: 4 May 2022].
  4. FastNetmon. [online]. Available at: <https://fastnetmon.com/> [Accessed: 3 Sept. 2022].
  5. Hameed, S., Ali, U., 2018. “HADEC: hadoop-based live DDoS detection framework”, EURASIP Journal on Information Security, vol. 2018, no. 1, p. 11. https://doi.org/10.1186/s13635-018-0081-z.
    https://doi.org/10.1186/s13635-018-0081-z
  6. Ghafar, A. Jaafar, Shahidan, M. Abdullah, Saifuladli Ismail, 2019. “Review of Recent Detection Methods for HTTP DDoS Attack” Journal of Computer Networks and Communications, vol. 2019, Article ID 1283472, 10 pages, https://doi.org/10.1155/2019/1283472.
    https://doi.org/10.1155/2019/1283472
  7. Behal, S., Kumar, K., Sachdeva, M., 2018. “D-FACE: an anomaly based distributed approach for early detection of DDoS attacks and flash events”. Journal of Network and Computer Applications, vol. 111, pp. 49-63.
    https://doi.org/10.1016/j.jnca.2018.03.024
  8. Singh, K. Singh, P., Kumar, K., 2018. “User behavior analytics-based classification of application layer HTTP-GET flood attacks,” Journal of Network and Computer Applications, vol. 112, pp. 97-114.
    https://doi.org/10.1016/j.jnca.2018.03.030
  9. Sreeram, I., Vuppala, V.P.K., 2017. “HTTP flood attack detection in application layer using machine learning metrics and bio inspired bat algorithm,” Applied Computing and Informatics, 15(1), DOI:10.1016/j.aci.2017.10.003.
    https://doi.org/10.1016/j.aci.2017.10.003
  10. Aborujilah, A. Musa, S., 2017. “Cloud-based DDoS HTTP attack detection using covariance matrix approach,” Journal of Computer Networks and Communications, vol. 2017, Article ID 7674594, 8 p.
    https://doi.org/10.1155/2017/7674594
  11. Snort – Network Intrusion Prevention and Detection System. [online]. Available at: <https://www.findbestopensource.com/product/snort> [Accessed: 5 Sept. 2022].
  12. Fail2ban – Daemon to ban hosts that cause multiple authentication errors. [online]. Available at: <https://www.findbestopensource.com/product/fail2ban-fail2ban> [Accessed: 23 Apr. 2022].
  13. Fuzzdb – Dictionary of attack patterns and primitives for black-box application fault injection and resource discovery. [online]. Available at: <https://www.findbestopensource.com/product/fuzzdb-project-fuzzdb> [Accessed: 23 Apr. 2022].
  14. OWASP. Owasp modsecurity core rule set project. [online]. Available at: <https://www.owasp.org/index.php/> [Accessed: 3 Sept. 2022].
  15. Betarte, G., Pardo, A., Martínez, R., 2018. “Web Application Attacks Detection Using Machine Learning Techniques,” 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1065-1072, DOI: 10.1109/ICMLA.2018.00174.
    https://doi.org/10.1109/ICMLA.2018.00174
  16. Ranum, M.J., Landfield, K., Stolarchuk, M., Sienkiewicz, M., Lambeth, A., Wall, E., 1997. “Implementing a generalized tool for network monitoring”. In Proceedings of the Eleventh Systems Administration Conference (LISA ’97) (San Diego, CA).
  17. Paxson, V., 1998. “Bro: A system for detecting network intruders in real-time”. In Proceedings of the 7th USENIX Security Symposium (San Antonio, TX).
  18. Internet Security Systems, Inc. RealSecure. 1997. [online]. Available at: <http://www.iss.net/prod/rsds.html> [Accessed: 3 Sept. 2022].
  19. Cisco Systems Inc. NetRanger – Enterprise-scale, Real-time, Network Intrusion Detection System. 1998. [online]. Available at: <http://www.cisco.com/warp/public/751/netranger/netra_ds.htm> [Accessed: 3 Sept. 2022].

Received  01.11.2022