Anomaly Detection in Cybersecurity with Graph-Based Approaches

The field of cybersecurity is changing dramatically in this dynamic age of digital revolution. This work on Anomaly Detection in Cybersecurity using Graph- Based Approaches represents a ground- breaking project that uses Graph Neural Networks' (GNNs'), Graph-Based Behavioural Anomaly Detection (GBBAD), Behavioural Identification Graph (BIG) and Graph-Based Botnet Detection (GBBD) capabilities to revolutionize the way we defend our digital borders. The discovery signifies a noteworthy progress in uncovering abnormalities. The precision and flexibility of this system has been emphasized by the ability to identify minute anomalies within intricate network interactions. Graph based techniques locating nodes or edges diverging from the normal behaviour of a graph carry out anomaly detection on graphs. There are several cyber security threats including fraud, malware incursions and network attacks that can be detected using graph-based anomaly detection methods. However, there are still some areas that need more attention. For instance, one possibility is to utilize the graph-based algorithms for pre-filtering alerts from firewalls and other cybersecurity systems. Such development would significantly reduce the workload for security analysts as well as improve overall security posture. In this research work an overview of current methods of detecting anomalies on cyber security using graphs has been presented. Key Words: Graph-Based Anomaly Detection (GBAD), Graph Neural Networks (GNNs), Graph-Based Behavioural Anomaly Detection (GBBAD), Graph-Based Botnet Detection (GBBD), Types of Anomalies, Availabilities of Data Levels.