SECUREVENT: Hybrid AI/ML Security Monitoring for Distributed Event-Based Systems

2026-06-01

AI Summary

Distributed event-driven systems exhibit an expanded attack surface due to their loose coupling and asynchronous communication, rendering traditional static security mechanisms inadequate for detecting anomalies in dynamically evolving event streams, identities, and temporal relationships. This work proposes a hybrid security monitoring architecture that integrates conventional access control with AI/ML techniques, uniquely combining graph-aware behavioral modeling, complex event processing (CEP), and federated learning. The framework further incorporates authenticated transport, topic-level authorization, event signing, and adversarial machine learning governance to enable real-time anomaly detection in dynamic environments. Experimental results demonstrate that the approach significantly improves recall under synthetic attack scenarios while maintaining a low false positive rate, thereby validating the efficacy and necessity of model-driven monitoring in dynamic event-based systems.

Abstract

Distributed event-based systems have become a common substrate for Internet-scale publish/subscribe services, IoT telemetry, cloud-native microservices, and security operations pipelines. Their loose coupling and asynchronous delivery improve scalability, but they also expand the attack surface: publishers, brokers, subscribers, topics, schemas, and temporal ordering can each be abused without a single component observing the whole behavior. This paper proposes SECUREVENT, a hybrid AI/ML security-monitoring architecture for distributed event-based systems. The architecture combines traditional protections such as authenticated transport, topic-level authorization, and signed events with online anomaly detection, graph-aware behavioral features, complex-event policy rules, federated learning, and adversarial-ML governance. A deterministic prototype study over synthetic event-stream attacks illustrates how a hybrid AI/CEP monitor can improve recall over static rules while retaining a low false-positive rate. The central claim is not that machine learning replaces cryptographic and access-control mechanisms, but that model-based security monitoring is necessary when event flows, identities, schemas, and timing relationships are too dynamic for static controls alone.

Problem

• distributed event-based systems
• attack surface
• security monitoring
• dynamic event flows
• anomaly detection

Innovation

• hybrid AI/ML
• event-based systems
• anomaly detection
• federated learning
• adversarial-ML governance