Adaptive Federated Intrusion Detection Framework Using Quantum-Enhanced Harris Hawks Optimization for Edge-Based 6G Networks

Abbas, Murtadha; Alogaili, Riyadh; Al-Sudani, Ahmed; Manickam, Ali

doi:10.25673/123623

Proceedings of International Conference on Applied Innovation in IT · 2026/03/31 · Vol. 14 · Issue 1 · pp. 727–740

Adaptive Federated Intrusion Detection Framework Using Quantum-Enhanced Harris Hawks Optimization for Edge-Based 6G Networks

Murtadha Talib Abbas, Riyadh Rahef Nuiaa Alogaili, Ahmed Raad Al-Sudani, Ali Hakem Alsaeedi and Selvakumar Manickam

📄 Download PDF DOI: 10.25673/123623

Abstract

The rapid evolution of 6G networks introduces unprecedented connectivity, speed, and data volume yet also heightens exposure to large-scale and intelligent cyberattacks. Traditional centralized intrusion detection systems are increasingly inadequate due to scalability limits, privacy risks, and latency challenges in distributed architectures. To overcome these constraints, this study proposes a Quantum-Enhanced Harris Hawks Optimization–based Federated Learning Intrusion Detection System (QHHO–FLIDS) that integrates quantum-driven feature selection with a hybrid CNN–LSTM framework deployed across edge nodes through federated learning. This approach enhances convergence efficiency, reduces data transfer, and preserves privacy by sharing encrypted model gradients instead of raw data. Extensive experiments using the CSE-CIC-IDS2018 and TON_IoT (2020) datasets confirm the system’s effectiveness, achieving detection accuracy above 99% with inference latency under 30 milliseconds. These results demonstrate that QHHO–FLIDS provides a lightweight, transparent, and adaptive security layer, offering significant implications for privacy-preserving intrusion detection and proactive threat mitigation in 6G-enabled cyber-physical environments.

Keywords

Federated Learning Intrusion Detection System 6G Networks Quantum-Enhanced Optimization Harris Hawks Optimization Explainable AI CNN–LSTM Cybersecurity.

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