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

• URL: http://arxiv.org/abs/2606.01741v1
• Date: Mon, 01 Jun 2026 06:04:16 GMT
• Title: SECUREVENT: Hybrid AI/ML Security Monitoring for Distributed Event-Based Systems
• Abstract summary: This paper proposes SECUREVENT, a hybrid AI/ML security-monitoring architecture for distributed event-based systems.<n>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.
• Score: 0.8557392136621891
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Intent-based Security Management Using the TM Forum TR292I Security Ontology [0.0]
  This paper presents a declarative, autonomous, self-protecting framework based on our design and standardization of the TM Forum TR292I Security Ontology v4.0.0.<n>Our approach leverages Description Logic (DL) and automated graph reasoning within a closed-loop execution pipeline to dynamically neutralize live threats.<n>We validate our model-driven architecture through a structural formal verification walkthrough of a distributed Denial of Service (DDoS) attack mitigation sequence on a disaggregated Next-Generation NodeB slice, demonstrating how automated reasoning resolves runtime constraint conflicts without human intervention.
  arXiv  Detail & Related papers  (2026-05-26T22:35:01Z)
• From CRUD to Autonomous Agents: Formal Validation and Zero-Trust Security for Semantic Gateways in AI-Native Enterprise Systems [0.0]
  This paper proposes the design, formal validation, and empirical evaluation of a Semantic Gateway governed by the Model Context Protocol (MCP)<n>The gateway reframes the enterprise API as a semantic surface where tools are dynamically discovered, authorized, and executed based on intent and policy enforcement.<n>The architecture introduces a three-layer Zero-Trust security model comprising a pre-inference Semantic Firewall, deterministic Tool-Level RBAC, and out-of-band Cryptographic Human-in-the-Loop approval.
  arXiv  Detail & Related papers  (2026-04-28T12:25:06Z)
• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents [60.98294016925157]
  AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss.<n>We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content.<n>Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks.
  arXiv  Detail & Related papers  (2026-01-14T23:06:35Z)
• Agentic AI for Autonomous Defense in Software Supply Chain Security: Beyond Provenance to Vulnerability Mitigation [0.0]
  The current paper includes an example of agentic artificial intelligence (AI) based on autonomous software supply chain security.<n>It combines large language model (LLM)-based reasoning, reinforcement learning (RL), and multi-agent coordination.<n>Results show that agentic AI can facilitate the transition to self defending, proactive software supply chains.
  arXiv  Detail & Related papers  (2025-12-29T14:06:09Z)
• MCPGuard : Automatically Detecting Vulnerabilities in MCP Servers [16.620755774987774]
  The Model Context Protocol (MCP) has emerged as a standardized interface enabling seamless integration between Large Language Models (LLMs) and external data sources and tools.<n>This paper systematically analyzes the security landscape of MCP-based systems, identifying three principal threat categories.
  arXiv  Detail & Related papers  (2025-10-27T05:12:51Z)
• Adaptive Attacks on Trusted Monitors Subvert AI Control Protocols [80.68060125494645]
  We study adaptive attacks by an untrusted model that knows the protocol and the monitor model.<n>We instantiate a simple adaptive attack vector by which the attacker embeds publicly known or zero-shot prompt injections in the model outputs.
  arXiv  Detail & Related papers  (2025-10-10T15:12:44Z)
• Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI System [2.5145802129902664]
  The MAESTRO framework was used to expose, evaluate, and eliminate vulnerabilities of agentic AI.<n>The prototype agent system was constructed and implemented, using Python, LangChain, and telemetry in WebSockets.
  arXiv  Detail & Related papers  (2025-08-12T00:14:12Z)
• DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents [52.92354372596197]
  Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities.<n>This interaction also introduces the risk of prompt injection attacks, where malicious inputs from external sources can mislead the agent's behavior.<n>We propose a Dynamic Rule-based Isolation Framework for Trustworthy agentic systems, which enforces both control and data-level constraints.
  arXiv  Detail & Related papers  (2025-06-13T05:01:09Z)
• SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic [49.549771439609046]
  We propose SISSA, a SOME/IP communication traffic-based approach for modeling and analyzing in-vehicle functional safety and cyber security. Specifically, SISSA models hardware failures with the Weibull distribution and addresses five potential attacks on SOME/IP communication. Extensive experimental results show the effectiveness and efficiency of SISSA.
  arXiv  Detail & Related papers  (2024-02-21T03:31:40Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.