XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT
arXiv:2606.00134v1 Announce Type: cross Abstract: Intrusion Detection Systems (IDS) in Internet of Things (IoT) environments face significant challenges due to data heterogeneity, lack of labeled data, and limited model interpretability. Federated Learning (FL) offers a privacy-preserving solution; however, existing approaches such as SOH-FL suffer from two key limitations: reliance on a manually tuned aggregation parameter {\gamma} and lack of explainability in model predictions. In this paper, we propose XAI-SOH-FL, an enhanced framework that integrates adaptive aggregation and explainable a
The proliferation of IoT devices and the increasing sophistication of cyber threats necessitate advanced, privacy-preserving security solutions, making the development of explainable federated learning for intrusion detection timely.
This development addresses critical challenges in IoT security, specifically data privacy, heterogeneity, and the need for interpretability in AI-driven intrusion detection systems, which is crucial for trust and adoption in sensitive environments.
The proposed XAI-SOH-FL framework introduces adaptive aggregation and explainable AI to federated learning for IoT-based intrusion detection, enhancing its effectiveness and transparency compared to previous opaque and static approaches.
- · IoT device manufacturers
- · Cybersecurity firms
- · Privacy-focused organizations
- · AI/ML researchers
- · Traditional IDS providers
- · Cyber attackers targeting IoT
Improved security posture for heterogeneous IoT environments through privacy-preserving and explainable AI.
Increased adoption of federated learning in critical infrastructure and sensitive data applications due to enhanced trust and interpretability.
New regulatory frameworks and industry standards emphasizing explainable AI and privacy-preserving techniques for cybersecurity solutions.
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