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This research proposes a federated learning framework that combines Explainable AI (XAI) with cognitive analytics to address privacy and scalability challenges in distributed cybersecurity. By training local models and sharing only encrypted parameters, the approach mitigates data leakage risks inherent in centralized intrusion detection while maintaining transparency through SHAP and LIME techniques. The study validates this architecture using standard intrusion detection datasets, offering a practical path forward for securing IoT and edge-based infrastructure without compromising model interpretability.
Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems
arXiv:2606.05701v1 Announce Type: cross Abstract: The increasing adoption of distributed infrastructure systems, cloud computing, Internet of Things (IoT) technologies, and edge-based architectures has significantly expanded the cybersecurity attack surface and introduced increasingly sophisticated cyber threats. Conventional centralized intrusion detection approaches often face challenges related to scalability, data privacy, communication overhead, and limited transparency in artificial intelligence-driven decision-making processes. To address these limitations, this study proposes a Cognitive Threat Intelligence and Explainable Federated Security Analytics framework for distributed infrastructure systems. The proposed framework integrates Federated Learning (FL), Explainable Artificial Intelligence (XAI), and cognitive cybersecurity analytics to enable collaborative and privacy-preserving cyber threat detection across distributed network environments. Instead of transmitting sensitive raw network traffic data to centralized servers, local security models are independently trained at distributed nodes, where only encrypted model parameters and updates are shared through a federated aggregation mechanism. This decentralized learning architecture improves privacy protection while reducing communication dependency and centralized security risks. To enhance intelligent threat analysis, the framework incorporates machine learning and deep learning algorithms including Random Forest, XGBoost, Autoencoder
Abstract:The increasing adoption of distributed infrastructure systems, cloud computing, Internet of Things (IoT) technologies, and edge-based architectures has significantly expanded the cybersecurity attack surface and introduced increasingly sophisticated cyber threats. Conventional centralized intrusion detection approaches often face challenges related to scalability, data privacy, communication overhead, and limited transparency in artificial intelligence-driven decision-making processes. To address these limitations, this study proposes a Cognitive Threat Intelligence and Explainable Federated Security Analytics framework for distributed infrastructure systems. The proposed framework integrates Federated Learning (FL), Explainable Artificial Intelligence (XAI), and cognitive cybersecurity analytics to enable collaborative and privacy-preserving cyber threat detection across distributed network environments. Instead of transmitting sensitive raw network traffic data to centralized servers, local security models are independently trained at distributed nodes, where only encrypted model parameters and updates are shared through a federated aggregation mechanism. This decentralized learning architecture improves privacy protection while reducing communication dependency and centralized security risks. To enhance intelligent threat analysis, the framework incorporates machine learning and deep learning algorithms including Random Forest, XGBoost, Autoencoder
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
C.2.0; C.2.3; I.2.6; K.6.5
arXiv:2606.05701 [cs.CR]
(or arXiv:2606.05701v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.05701 arXiv-issued DOI via DataCite (pending registration)
International Journal of Research and Technology (IJRT), Volume 13, Issue 01, January-March 2025, pp. 132-151
https://doi.org/10.64882/ijrt.v13.i1.1384 DOI(s) linking to related resources
From: Md. Arifur Rahman [view email] [v1] Thu, 4 Jun 2026 04:41:53 UTC (1,361 KB)
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