Analysis of Web Application Security Threats Using the DBSCAN Clustering Technique

As web applications continue to become more and more complex, they have become opportunistic for different types of security threats. These threats are usually represented by various attack types and for every attack it is necessary to choose the proper method and approaches to discover it. However, the lack of a structured approach to analyzing the relationships between security threats, attack types, and testing techniques hinders the development of efficient security strategies. By leveraging DBSCAN, this study groups similar security incidents to identify hidden relationships and trends that can inform more targeted security measures. The methodology entails gathering information from the latest high-quality research on web application security, clustering the security threats by employing DBSCAN algorithm, and then comparing the generated clusters to attack types, as well as with the various testing techniques. This analysis provides valuable insights into how different attack types are associated with specific security threats, and which testing techniques are most effective for detecting and mitigating these threats. The findings contribute towards a more structured approach to web application security, offering recommendations for improved threat detection and prevention strategies.