Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems

Computer Science > Cryptography and Security [Submitted on 4 Jun 2026] Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems Md. Arifur Rahman, B. M. Taslimul Haque, Md. Iqbal Hossan, Md. Serajul Kabir Chowdhury Rubel 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 Comments: 22 pages, 10 figures, 1 conceptual framework diagram, 1 methodology workflow diagram, empirical study using NSL-KDD and CIC-IDS2017 datasets, Federated Learning, Explainable AI (SHAP, LIME), cybersecurity and intrusion detection framework Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) MSC classes: 68M10, 68T07, 68T09, 94A60 ACM classes: C.2.0; C.2.3; I.2.6; K.6.5 Cite as: arXiv:2606.05701 [cs.CR]   (or arXiv:2606.05701v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.05701 Focus to learn more Journal reference: International Journal of Research and Technology (IJRT), Volume 13, Issue 01, January-March 2025, pp. 132-151 Related DOI: https://doi.org/10.64882/ijrt.v13.i1.1384 Focus to learn more Submission history From: Md. Arifur Rahman [view email] [v1] Thu, 4 Jun 2026 04:41:53 UTC (1,361 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)