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

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...

Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response

Autonomous network-security response systems promise to reduce Security Operations Centre SOC reaction latency, but reward-only multi-agent reinforcement learning MARL can improve security reward while remaining non-deployable. We present a safety-contract graph MARL framework and instantiate it ...

Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing

In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks, such as simulating heavy traffic with multiple devices running crowdsourced navigation applications, to mislead vehicles toward suboptimal routes and increase congestion. To addres...

LLM Meets the Sky: Heuristic Multi-Agent Reinforcement Learning for Secure Heterogeneous UAV Networks

This work tackles the physical layer security PLS problem of maximizing the secrecy rate in heterogeneous UAV networks HetUAVNs under propulsion energy constraints. Unlike prior studies that assume uniform UAV capabilities or overlook energy-security trade-offs, we consider a realistic scenario...