AI-Driven Predictive Analytics for Proactive Network Threat Detection

The medical industry now entirely depends upon cyber infrastructure and therefore is also one of the targets for malicious threats. Legacy defenses like firewalls, access control, and intrusion detection systems lack the responsiveness of dealing with continuously changing threats. As a consequence, all forms of security-related vulnerabilities lie with data, resource management, as well as regulations. Hence, this paper intends to recommend a predictive network threat detection approach utilizing AI models that can cater to real-time protection for health setups. The system combines several machine learning algorithms such as K-Nearest Neighbors (KNN), Decision Trees, Random Forest, Naïve Bayes, Logistic Regression, AdaBoost, and XGBoost to identify and classify cyber threats with high precision. The approach takes a number of important steps: data gathering, where network traffic information is harvested from real-time observation and publicly accessible datasets; feature extraction, where appropriate properties like packet size, protocol category, and session length are yielded to improve the accuracy of classification; and training and testing the model, wherein machine learning strategies are used in order to discern normal and undesirable network traffic.Every algorithm lends itself differently towards the detection step.