Graph Neural Network (GNN) for Cyber Security
Graph Neural Networks (GNNs) have recently gained attention in the field of cyber security due to their ability to analyze network data in the form of graphs. GNNs provide a novel approach to detect anomalies and malicious activities within network graphs.
When viewed as a graph, a network of computers can be analyzed with GNNs for anomaly detection. GNNs can identify patterns of behavior that deviate from normal network activity. This can include identifying unusual traffic patterns, abnormal resource usage, or unexpected user behavior. Anomalies within provenance graphs often correlate to malicious activity within the network. For example, an attacker might use a compromised user account to access sensitive information or execute malicious code on a network node.
GNNs have been used to identify these anomalies on individual nodes and within paths to detect malicious processes, or on the edge level to detect lateral movement. By analyzing the connections between nodes in the graph, GNNs can identify suspicious network behavior that would be difficult to detect using traditional security solutions. For example, GNNs can identify lateral movement, where an attacker moves from one compromised node to another in an attempt to evade detection.
In addition to anomaly detection, GNNs can also be used for threat intelligence and vulnerability assessment. GNNs can analyze the structure of the network graph to identify potential vulnerabilities and weaknesses. This can help security teams proactively identify and address security risks before they are exploited by attackers.
Convolutional Neural Networks (CNNs) and Transformers are among the popular algorithms used for Graph Neural Networks (GNNs).
