Applying Artificial Intelligence for Early Detection of Cyber Threats in Hospital Networks

Applying Artificial Intelligence for Early Detection of Cyber Threats in Hospital Networks

Abstract

Hospitals are using more technology connected to networks for taking care of patients and running smoothly, making hacking a big issue in healthcare. While important, technology can make hospitals vulnerable to ransomware attacks, data hacks, and Distributed Denial-of-Service attacks. Fixing these issues is important for providing essential services and protecting patients’ private information. Hospitals can improve their safety by using artificial intelligence. The paper will help them find and address cyber threats early instead of just reacting to them with focusing on the Deep Neural Network (DNN) model for its ability to capture complex network patterns. Additionally, K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM) are employed as comparative models to evaluate their effectiveness in identifying cyber threats. I like this subject because it combines my passion for science with solving real problems. AI can help keep healthcare systems secure, which builds trust with patients and ensures important medical services keep running.

Keywords: Cybersecurity; Artificial Intelligence; Hospital Networks; Intrusion Detection; Anomaly Detection.

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