SwarmSense-DNN: A Trustworthy and Decentralized Neural Framework for Proactive Anomaly Defense in Consumer IoT

2026-06-10

AI Summary

This work addresses the challenge of trustworthy anomaly detection in consumer-grade IoT systems under AI-driven cyber threats by proposing a decentralized neural framework grounded in swarm intelligence. The approach overcomes critical limitations of conventional centralized methods—namely communication bottlenecks, single points of failure, and privacy vulnerabilities—by uniquely integrating swarm intelligence, hierarchical federated learning, graph neural networks, and attention mechanisms to jointly model local and global anomalous behaviors. Data privacy and system robustness are further enhanced through differential privacy and decentralized agent collaboration. Experimental results across five benchmark datasets demonstrate that the proposed framework achieves an average detection accuracy of 95.44%, reduces communication overhead by 67%, and exhibits strong fault tolerance and resilience against adversarial attacks.

Abstract

The rapid growth of consumer IoT devices has introduced unprecedented challenges in trustworthy anomaly detection against AI-enabled cyber threats, requiring real-time, privacy-preserving, and scalable defense mechanisms. Traditional centralized strategies face critical limitations, including communication bottlenecks, single points of failure, and privacy vulnerabilities when processing distributed consumer data. We propose SwarmSense-DNN, a novel decentralized neural framework employing swarm intelligence for secure, cooperative anomaly detection across distributed IoT environments. The framework integrates autonomous agents with deep neural networks to form a self-organizing defense system that detects evolving anomalies without centralized coordination. It utilizes hierarchical federated learning with graph neural networks and attention mechanisms to capture local and global anomaly behaviors while ensuring data privacy. Extensive experiments demonstrate SwarmSense-DNN's superior performance: it achieves 95.44% average detection accuracy across five benchmark datasets while reducing communication overhead by 67%. The framework maintains robust resilience against adversarial threats through differential privacy safeguards and demonstrates strong fault tolerance under node failures and AI-enabled attacks.

Problem

• consumer IoT
• anomaly detection
• AI-enabled cyber threats
• privacy-preserving
• decentralized defense

Innovation

• decentralized neural framework
• swarm intelligence
• federated learning
• graph neural networks
• differential privacy