arXiv:2512.06906v2 Announce Type: replace-cross Abstract: Detecting the anomalies of web applications, important infrastructures for running modern companies and governments, is crucial for providing reliable web services. Many modern web applications operate on web APIs (e.g., RESTful, SOAP, and WebSockets), their exposure invites intended attacks or unintended illegal visits, causing abnormal system behaviors. However, such anomalies can share very similar logs with normal logs, missing crucial information (which could be in database) for log discrimination. Further, log instances can be als
The increasing complexity and exposure of web applications and APIs necessitate more sophisticated and explainable anomaly detection, moving beyond simple log analysis as attackers become more subtle.
Reliable web services are critical infrastructure for modern economies, and robust API security, including explainable anomaly detection, directly impacts operational stability and national security.
This research introduces a method for explainable anomaly detection in web APIs, moving beyond simple log-based methods by inferring invariants and providing actionable insights for security professionals.
- · Cybersecurity companies
- · Web application developers
- · Governments
- · Financial institutions
- · Cyber attackers
- · Legacy security vendors
- · Companies with weak API security
Web applications and APIs become more resilient against sophisticated attacks and unintended behaviors.
Reduced incidence of data breaches and service disruptions, leading to increased trust in digital infrastructure.
The development of AI for defensive cybersecurity accelerates, creating a new arms race in security intelligence.
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Read at arXiv cs.LG