**4.1 Cybersecurity for AI**

Artificial Intelligence (AI) and machine learning are playing an important role in cybersecurity. AI-based cybersecurity systems can provide a clear knowledge of global and healthcare industry security threats to help make critically important decisions in a critical situation. AI techniques are expected to enhance cybersecurity by assisting human system managers with automated monitoring, analysis, and responses to adversarial attacks.

The research outcomes from the integrated AI and cybersecurity can lead to an extensive change in the understanding of the basis of cybersecurity. Also, this integrated results can help to motivate and educate healthcare providers about

cybersecurity in the age of AI in an innovative way. Fundamental research in AI together with cybersecurity research might expand existing AI opportunities and resources in cybersecurity analytics and workforce development. AI relies on innovations like Machine Learning, Deep Learning, Natural Language Processing, and so forth to make it hard for malicious actors to access servers and other important data. AI has crossed many milestones and now it is turning towards cybersecurity. According to MIT, AI can detect about 85% of cyberattacks and help to secure IoT and CPS systems including the healthcare industry from cyberattacks. The prototype AI-based cybersecurity system is shown in **Figure 4**.

AI, Machine Learning (ML), and Deep Learning (DL) are overlapping and someone can easily get confused with these terminologies. The AI technique can help computers to mimic human behavior. The machine learning is a subset of AI, which give computers to automatically learn models and representation of the data sets. The deep learning is a subset of machine learning that help computers to solve multi-layer neural network complex problems. Use AI and leveraging machine learning and deep learning techniques are the smart choice to extract and analyze the sensory data from a smart IoT system. The researchers in [31] evaluate the performance of eleven famous ML and DL algorithms using six IoT related data sets. The authors of this paper showed that considering their performance evaluation matrics, including precision, recall, f1-score, accuracy, execution time, area under receiver operating characteristic curve (ROC-AUC) score, and confusion matrix, Random Forest performed better than other ML models. Also, they showed that ANN and CNN have interesting results comparing with other deep learning models.
