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

SECUREVENT proposes a hybrid AI/ML security architecture for distributed event-based systems that combines cryptographic controls with anomaly detection and behavioral analysis. The system addresses vulnerabilities in publish/subscribe platforms, IoT networks, and microservices by monitoring complex event patterns that static rules cannot detect, demonstrating improved threat detection recall while maintaining low false-positive rates.

SECUREVENT addresses a critical security gap in modern distributed systems where traditional perimeter-based defenses fail. As organizations increasingly rely on event-driven architectures for IoT telemetry, cloud-native applications, and security operations, the asynchronous nature of these systems creates blind spots—attackers can manipulate publishers, brokers, topics, or temporal ordering without any single component observing the full attack chain. This architectural vulnerability becomes more pressing as systems scale across geographic regions and organizational boundaries.

The research reflects a broader industry trend: static security controls are insufficient for dynamic, distributed environments. Enterprises have invested heavily in cryptographic protections and role-based access controls, yet these mechanisms operate at component boundaries rather than across event flows. SECUREVENT's contribution is methodological rather than revolutionary—it proposes layering AI/ML anomaly detection and complex-event processing on top of existing controls, creating a defense-in-depth strategy where machine learning identifies behavioral deviations that signature-based rules miss.

For cloud-native infrastructure operators and security teams, this work validates the business case for behavioral monitoring platforms. The prototype demonstrates that federated learning can detect coordinated attacks across broker networks while maintaining privacy constraints. Organizations managing large-scale Kafka clusters, AWS EventBridge deployments, or multi-tenant IoT platforms should evaluate whether their current monitoring stacks (typically log aggregation plus basic alerting) can detect event-level anomalies.

Looking forward, adoption depends on tooling maturity. Open-source implementations of graph-aware behavioral features and adversarial-ML governance in production environments remain limited. Security vendors and cloud providers will likely integrate these concepts into managed event platforms over the next 12-18 months.

• →Distributed event systems require behavioral monitoring beyond cryptographic and access-control mechanisms due to their asynchronous, loosely-coupled architecture.
• →Hybrid AI/ML monitoring combined with complex-event processing rules improves threat detection recall while maintaining low false-positive rates in synthetic benchmarks.
• →Federated learning enables anomaly detection across distributed brokers without centralizing sensitive event data.
• →Graph-aware behavioral features provide context for detecting coordinated attacks across publishers, subscribers, and topics.
• →This approach validates market demand for behavioral security platforms in cloud-native and IoT infrastructure environments.