Predictive Analytics-Enabled Cyber Attack Detection

Cyber-attacks are becoming increasingly sophisticated and difficult to detect using traditional security measures. To address this challenge, we propose a predictive analytics- enabled cyber-attack detection system that utilizes machine learning algorithms to analyze network traffic and identify potential security threats in real time. Our system uses a combination of supervised and unsupervised learning techniques to identify patterns and anomalies in network data, and to generate anomaly and normal alert. The system is trained using historical data from known cyber-attacks and anomalies and we visualize the accuracy of various algorithms.