Cybersecurity has become a pervasive issue affecting all aspects of our digital lives, from financial institutions to healthcare systems (Prince, 2018). It encompasses technical, organizational, and executive measures to protect electronic information and communication systems from unauthorized access and misuse (Mijwil, 2023). The healthcare sector is particularly vulnerable to cybercrime due to its valuable data and weak defenses, with potential breaches impacting patient safety and trust (Coventry & Branley, 2018). To address these challenges, artificial intelligence, specifically machine learning and deep learning techniques, plays a significant role in predicting and understanding malicious software behavior (Mijwil, 2023). Researchers and practitioners from academia, government, and industry are developing emerging technologies and methodologies to mitigate various cyberthreats (Hsu et al., 2015). As cybersecurity becomes integral to patient safety and overall digital security, a holistic approach involving changes to human behavior, technology, and processes is necessary (Coventry & Branley, 2018).
Reference:
- Prince, D. (2018). Cybersecurity: The security and protection challenges of our digital world. Computer, 51(4), 16-19.
- Mijwil, M. M., Salem, I. E., & Ismaeel, M. M. (2023). The significance of machine learning and deep learning techniques in cybersecurity: A comprehensive review. Iraqi Journal For Computer Science and Mathematics, 4(1), 10.
- Coventry, L., & Branley, D. (2018). Cybersecurity in healthcare: A narrative review of trends, threats and ways forward. Maturitas, 113, 48-52.
- Hsu, D. F., Marinucci, D., & Voas, J. M. (2015). Cybersecurity: Toward a Secure and Sustainable Cyber Ecosystem. Computer, 48(4), 12-14.
- Coventry, L., & Branley, D. (2018). Cybersecurity in healthcare: A narrative review of trends, threats and ways forward. Maturitas, 113, 48-52.
