Agentic SecPBFT Agentic AI-Driven Proactive Security Framework for Wireless PBFT Consensus in Mobile Ad-Hoc Networks

Jul 16, 2026

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The work analyzes how Practical Byzantine Fault Tolerance (PBFT), originally built for stable wired deployments, becomes vulnerable in mobile ad-hoc networks under intelligent coordinated threats including Sybil attacks, Byzantine collusion, and message manipulation. It proposes Agentic-SecPBFT by equipping each consensus node with an agent in a distributed multi-agent system. Agents use a hierarchical Multi-Agent Deep Q-Network approach to learn real-time proactive policies using local behavior, message consistency, and maintained reputation scores. Extensive modeling and simulations show a 95.0% attack-detection rate with 1.8% false positives, improving throughput and latency versus PBFT variants under malicious-node conditions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

Introduction

Background

Research Challenges

Agentic-SecPBFT: Agentic AI-Driven Proactive Security Framework for Wireless PBFT Consensus in Mobile Ad-Hoc Networks Haoxiang Luo, Yinqiu Liu, Ruichen Zhang, Guangyuan Liu, Gang Sun, Senior Member, IEEE, Hongfang Yu, Senior Member, IEEE, Zhu Han, Fellow, IEEE, and Dong In Kim, Life Fellow, IEEE arXiv :2607 .03269v 1 [ cs .NI] 3 Jul 2026 Abstract—The standard Practical Byzantine Fault Tolerance (PBFT) protocol, designed for stable, wired environments, exhibits critical vulnerabilities when deployed in settings like mobile ad-hoc networks, thus making it susceptible to sophisticated threats such as Sybil attacks, Byzantine collusion, and message manipulation. Existing static defense mechanisms are illequipped to handle the intelligent and coordinated nature of these attacks. To address this challenge, this paper leverages the Agentic AI paradigm to build a distributed multi-agent system in which each consensus node is equipped with an intelligent agent. These agents employ a hierarchical Multi-Agent Deep QNetwork (MADQN) algorithm to learn and execute proactive security policies in real-time. By observing local network behavior, message consistency, and dynamically maintained reputation scores, the agents collaboratively identify suspicious behavior and recommend defensive actions under standard PBFT quorum and membership rules, thereby improving the integrity of the consensus process. We refer to the resulting framework as Agentic-SecPBFT. Then, we formally model key attack vectorsand conduct extensive simulations. The results demonstrate that Agentic-SecPBFT reaches a 95.0% attack detection rate with a 1.8% false positive rate. Compared with mainstream PBFT variants, it achieves 3.1× higher throughput with 56% lower latency on average under 33% malicious nodes, offering a robust and adaptive security solution for decentralized wireless systems. Index Terms—Wireless consensus, PBFT, network security, agentic AI, multi-agent deep reinforcement learning (MADRL). I. INTRODUCTION A. Background THE rapid proliferation of the Internet of Things (IoT) and the evolution of autonomous systems have catalyzed a fundamental architectural shift from centralized cloud computing to decentralized edge intelligence [1] . In this decentralized landscape, ensuring data integrity, traceability, and trust among H. Luo is with the WeBank-NTU Joint Research Institute on Fintech, Nanyang Technological University, Singapore 639798, and also with the College of Computing and Data Science, Nanyang Technological University, Singapore 639798 ([e-mail:haoxiang.luo@ntu.edu.sg](e-mail:haoxiang.luo@ntu.edu.sg)). Y. Liu, R. Zhang, and G. Liu are with the College of Computing and Data Science, Nanyang Technological University, Singapore 639798 ([e-mail: yinqiu001@e.ntu.edu.sg](e-mail: yinqiu001@e.ntu.edu.sg); [ruichen.zhang@ntu.edu.sg](ruichen.zhang@ntu.edu.sg); [liug0022@e.ntu.edu.sg](liug0022@e.ntu.edu.sg).) G. Sun (corresponding author) and H. Yu are with the School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: {gangsun, [yuhf](yuhf}@uestc.edu.cn)[}](yuhf}@uestc.edu.cn)[@uestc.edu.cn](yuhf}@uestc.edu.cn)). Z. Han is with the Electrical and Computer Engineering Department, University of Houston, Houston, TX 77004, USA (email: [hanzhu22@gmail.com](hanzhu22@gmail.com)). D. I. Kim is with the Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea (e-mail: don[gin@skku.edu](gin@skku.edu)). inherently trustless entities is paramount [2] . Blockchain technology, with its immutable ledger and distributed consensus mechanisms, has emerged as a foundational layer for these next-generation networks. Specifically, in wireless ad hoc networks such as Vehicular Ad Hoc Networks (VANETs) and Industrial IoT (IIoT) [3], [4], blockchain enables secure Peerto-Peer (P2P) transactions and trusted data sharing without reli

FAQs

Why does standard PBFT face security and performance issues in mobile ad-hoc networks?

PBFT was designed for static wired environments; in wireless settings, unreliable links and timeouts can be caused by fading or interference rather than malicious silence. This misinterpretation triggers costly view-change procedures and degrades throughput while leaving openings for sophisticated attacks.

How does Agentic-SecPBFT detect and respond to attacks?

Agentic-SecPBFT uses a distributed multi-agent system where each consensus node runs an intelligent agent. Agents learn proactive security policies with a hierarchical multi-agent deep Q-network, observing local network behavior, message consistency, and reputation scores to identify suspicious actions and recommend defenses under PBFT quorum and membership rules.

What performance results does Agentic-SecPBFT achieve compared with PBFT variants?

Simulations show a 95.0% attack detection rate with a 1.8% false-positive rate. Compared with mainstream PBFT variants, it provides 3.1× higher throughput and 56% lower average latency under scenarios with 33% malicious nodes.

Jul 16, 2026

Upload by Eliana