# A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems
###### 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 cybercime. 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. d 2021 13 90 11030009 11030009 11030009 11030009 11030009 11030009 11030009 11030009 11030009 11030009 1103009 11030009 1103009 1103009 1103009 1103009  
from both outsiders as well as malicious insiders. Although many security preventive measures are encountered in all healthcare sectors, statistics still prove that there is always a successful attack [8]. Hence, it is crucial to have detection measures to detect any tampering with data. Detection is usually done by the intrusion detection system (IDS). Once an IDS detects an attack, recovery should start to roll back any changes to ensure the integrity of the data. As the world moves toward the Internet of things (IoT) era, companies and organizations are replacing traditional paper-based data with new electronic data to achieve higher efficiency in processing. Online data provides availability and ease of sharing, but on the other hand, it poses privacy and security concerns. This paper mainly focuses on healthcare systems, where data needs to be highly available to doctors and nurses to help them do their job, as downtime may be life-threatening to patients. In addition to the immediate availability of data, it should also be secure and correct. Any intentional or unintentional misinformation can jeopardize patients' health and, potentially, their lives. Information about patients, such as medication, allergies, surgeries, medical conditions, and medical history, is confidential and should only be available to authorized personnel. Hence, the CIA triad becomes essential to any implemented system. Last year, more than 8 billion malicious data attacks occurred [9]. In addition, 89% of healthcare organizations experienced data breaches in the past two years [9]. Healthcare databases are precious targets for attackers because they facilitate identity theft and cybercrimes. Any denial of service of healthcare databases affects patients' lives. The most critical metric to be considered while securing healthcare databases is speed. A patient's history should be retrieved as soon as possible, and healthcare transactions should not be affected for a long time by an attack. Hence, database assessment for the healthcare system recovery should be efficient to prevent long downtimes while performing these procedures. The objective of this paper is to develop an efficient algorithm to detect malicious transactions on a patient's data.