AI-Based Zero-Day Attack Detection using Graph Neural Networks

Abstract

Zero-day attacks refer to advanced cyberattacks where there is exploitation of vulnerabilities that have not been discovered before and hence cannot be detected by signature-based and rule-based security systems. In this project, an AI-Based Zero-Day Attack Detection System Using Graph Neural Networks will be developed to detect any unusual and suspicious activity within the network. In this system, the different network entities such as the user, device, IP address, process, and network communications are modeled as nodes while the interactions between them as edges in the graph. The GNN system models learn the complex relations and normal patterns of communications within the network and detects deviations which can be seen to show the presence of new kinds of attacks. The steps in this system include data acquisition, data preprocessing, feature engineering, graph construction, GNN model training, anomaly detection, risk assessment, and security alerting. Evaluation of the system performance will be done using Accuracy, Precision, Recall, F1-Score, and False Positive Rate.