Intelligent Intrusion Detection System for Healthcare Using Fuzzy Neural Networks

  • Rehab Flaih Hasan
  • shatha h. Jafer
Keywords: : Intrusion Detection, Healthcare systems, Machine Learning Techniques.


Healthcare facilities in the modern day are a significant problem, particularly in developing nations where distant locations are hampered by a scarcity of high-quality hospitals and medical professionals. As artificial intelligence has revolutionized several fields of life, it has also had a positive impact on health. The existing architecture is facing several issues for the conventional telemedicine store-and-forward method. These include the requirement for a local health centre with dedicated staff, the need for medical equipment to prepare patient reports, the time constraint of 24–48 hours in receiving diagnosis and medication details from a medical expert in the main hospital, the cost of local health centres, and the requirement for a Wi-Fi connection, among others. Medical gadgets equipped with wireless communication capabilities allow for remote monitoring and are becoming more linked to one another and the Internet. Internet of Intelligent Things-enabled Medical and connected medical devices, also known as IoMT, have allowed continuous real-time patient monitoring, improved diagnostic accuracy, and more effective therapy than ever before. Although these devices have various advantages, they also create new attack surfaces, resulting in an increased number of security and privacy risks, which must be addressed. Attacks against medical equipment that are linked to the Internet have the potential to inflict significant bodily injury and even death on the patients who are targeted. In this study, we examine the strategies that may be used to provide advice on how to safeguard a network of connected medical devices.

How to Cite
R. Flaih Hasan and shatha h. Jafer, “Intelligent Intrusion Detection System for Healthcare Using Fuzzy Neural Networks”, JMAUC, vol. 16, no. 1, pp. 72-80, May 2024.