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A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems

Author

Listed:
  • Sanaa Kaddoura

    (College of Technological Innovation, Zayed University, Abu Dhabi 144534, United Arab Emirates)

  • Ramzi A. Haraty

    (Department of Computer Science and Mathematics, Lebanese American University, Beirut 1102 2801, Lebanon)

  • Karam Al Kontar

    (Department of Computer Science and Mathematics, Lebanese American University, Beirut 1102 2801, Lebanon)

  • Omar Alfandi

    (College of Technological Innovation, Zayed University, Abu Dhabi 144534, United Arab Emirates)

Abstract

In the current Internet of things era, all companies shifted from paper-based data to the electronic format. Although this shift increased the efficiency of data processing, it has security drawbacks. Healthcare databases are a precious target for attackers because they facilitate identity theft and cybercrime. This paper presents an approach for database damage assessment for healthcare systems. Inspired by the current behavior of COVID-19 infections, our approach views the damage assessment problem the same way. The malicious transactions will be viewed as if they are COVID-19 viruses, taken from infection onward. The challenge of this research is to discover the infected transactions in a minimal time. The proposed parallel algorithm is based on the transaction dependency paradigm, with a time complexity O((M+NQ+N^3)/L) (M = total number of transactions under scrutiny, N = number of malicious and affected transactions in the testing list, Q = time for dependency check, and L = number of threads used). The memory complexity of the algorithm is O(N+KL) (N = number of malicious and affected transactions, K = number of transactions in one area handled by one thread, and L = number of threads). Since the damage assessment time is directly proportional to the denial-of-service time, the proposed algorithm provides a minimized execution time. Our algorithm is a novel approach that outperforms other existing algorithms in this domain in terms of both time and memory, working up to four times faster in terms of time and with 120,000 fewer bytes in terms of memory.

Suggested Citation

  • Sanaa Kaddoura & Ramzi A. Haraty & Karam Al Kontar & Omar Alfandi, 2021. "A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems," Future Internet, MDPI, vol. 13(4), pages 1-18, March.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:4:p:90-:d:527348
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    References listed on IDEAS

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    1. Ramzi Ahmed Haraty & Sanaa Kaddoura & Ahmed Zekri, 2017. "Transaction Dependency Based Approach for Database Damage Assessment Using a Matrix," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(2), pages 74-86, April.
    2. Dimitrios Papamartzivanos & Sofia Anna Menesidou & Panagiotis Gouvas & Thanassis Giannetsos, 2021. "A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly," Future Internet, MDPI, vol. 13(2), pages 1-34, January.
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    Cited by:

    1. Alessandro Midolo & Emiliano Tramontana, 2023. "An Automatic Transformer from Sequential to Parallel Java Code," Future Internet, MDPI, vol. 15(9), pages 1-19, September.
    2. Sanaa Kaddoura, 2022. "Evaluation of Machine Learning Algorithm on Drinking Water Quality for Better Sustainability," Sustainability, MDPI, vol. 14(18), pages 1-18, September.
    3. Namhla Mtukushe & Adeniyi K. Onaolapo & Anuoluwapo Aluko & David G. Dorrell, 2023. "Review of Cyberattack Implementation, Detection, and Mitigation Methods in Cyber-Physical Systems," Energies, MDPI, vol. 16(13), pages 1-25, July.
    4. Kumari, Pooja & Shankar, Amit & Behl, Abhishek & Pereira, Vijay & Yahiaoui, Dorra & Laker, Benjamin & Gupta, Brij B. & Arya, Varsha, 2024. "Investigating the barriers towards adoption and implementation of open innovation in healthcare," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    5. Anupama Mishra & Neena Gupta & Brij B. Gupta, 2023. "Defensive mechanism against DDoS attack based on feature selection and multi-classifier algorithms," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 82(2), pages 229-244, February.

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