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Transaction Dependency Based Approach for Database Damage Assessment Using a Matrix

Author

Listed:
  • Ramzi Ahmed Haraty

    (Lebanese American University, Beirut, Lebanon)

  • Sanaa Kaddoura

    (Beirut Arab University, Beirut, Lebanon)

  • Ahmed Zekri

    (Beirut Arab University, Beirut, Lebanon)

Abstract

One of the critical concerns in the current era is information security. Companies are sharing vast online critical data, which exposes their databases to malicious attacks. When protection techniques fail to prevent an attack, recovery is needed. Database recovery is not a straightforward procedure, since the transactions are highly interconnected. Traditional recovery techniques do not consider the interconnection between transactions because this information is not saved anywhere in the log file. Thus, they rollback all the transactions starting from the detected malicious transaction to the end of the log file. Hence, both affected and benign transactions will be rolled back, which is a waste of time. This paper presents an algorithm that works efficiently to assess the damage caused in the database by malicious transaction and recovers it. The proposed algorithm keeps track of the transactions that read from one another and store this information in a single matrix. The experimental results prove that the algorithm is faster than any other existing algorithm in this domain.

Suggested Citation

  • 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.
  • Handle: RePEc:igg:jswis0:v:13:y:2017:i:2:p:74-86
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSWIS.2017040105
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    Cited by:

    1. 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.
    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.

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