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Predictive model for multistage cyber-attack simulation

Author

Listed:
  • Ravdeep Kour

    (Luleå University of Technology)

  • Adithya Thaduri

    (Luleå University of Technology)

  • Ramin Karim

    (Luleå University of Technology)

Abstract

Adoption of information and communication technologies (ICT) in railway has improved the reliability, maintainability, operational efficiency, capacity as well as the comfort of passengers. This adoption introduces new vulnerabilities and entry points for hackers to launch attacks. Advanced cybersecurity threats with automated capabilities are increasing in such sectors as finance, health, grid, retail, government, telecommunications, transportation, etc. These cyber threats are also increasing in railways and, therefore, it needs for cybersecurity measures to predict, detect and respond these threats. The cyber kill chain (CKC) model is a widely used model to detect cyber-attacks and it consists of seven stages/chains; breaking the chain at an early stage will help the defender stop the adversary’s malicious actions. Due to lack of real cybersecurity data, this research simulates cyber-attacks to calculate the attack penetration probabilities at each stage of the cyber kill chain model. The objective of this research is to predict cyber-attack penetrations by implementing various security controls using modeling and simulation. This research is an extension of developed railway defender kill chain which provides security controls at each stage of CKC for railway organizations to minimize the risk of cyber threats.

Suggested Citation

  • Ravdeep Kour & Adithya Thaduri & Ramin Karim, 2020. "Predictive model for multistage cyber-attack simulation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 600-613, June.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:3:d:10.1007_s13198-020-00952-5
    DOI: 10.1007/s13198-020-00952-5
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