Real-Time Cyber Attack Detection in Healthcare Cyber-Physical Systems Using AI and Machine Learning

Radhey Sharma

doi:10.55544/ijrah.1.1.14

Authors

Radhey Sharma Jiwaji University, Gwalior, INDIA

DOI:

https://doi.org/10.55544/ijrah.1.1.14

Keywords:

Cyber-physical system (CPS), artificial intelligence (AI), healthcare, data normalization, jellyfish optimized weighted dropped binary long short-term memory (JFO-WDBLSTM) approach

Abstract

Cyberattack patterns may be predicted using AI models, and this information is processed to aid healthcare professionals in making decisions. The proposed system begins with a medical record and preprocesses it using a normalization method. The novel jellyfish-optimized weighted dropped binary long short-term memory (JFO-WDB-LSTM) technique ultimately distinguishes between valid and erroneous healthcare data. Compared to other models, our suggested model achieves attack prediction ratios of 98%, detection accuracy ratios of 88%, delay ratios of 50%, and communication costs of 67%, according to experimental results.

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