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

• URL: http://arxiv.org/abs/2606.05701v1
• Date: Thu, 04 Jun 2026 04:41:53 GMT
• Title: Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems
• Abstract summary: This study proposes a Cognitive Threat Intelligence and Explainable Federated Security Analytics framework for distributed infrastructure systems.<n>The proposed framework integrates Federated Learning (FL), Explainable Artificial Intelligence (XAI), and cognitive cybersecurity analytics to enable collaborative and privacy-preserving cyber threat detection.<n>To enhance intelligent threat analysis, the framework incorporates machine learning and deep learning algorithms including Random Forest, XGBoost, Autoencoder.
• Score: 0.8496907566303933
• License: http://creativecommons.org/licenses/by/4.0/
• 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

Related papers

• Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework [0.8496907566303933]
  This study is based on the CICIDS 2017 dataset for the development and testing of intrusion detection system models and cyber risk prediction models based on machine learning.<n>The proposed framework presents the reliability and resilience of the model by having various performance measures such as accuracy, precision, recall, F1 score, ROC-AUC, and false positive rate.
  arXiv  Detail & Related papers  (2026-06-04T05:05:14Z)
• Privacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Framework [45.88028371034407]
  This paper presents a structured analysis of privacy-preserving techniques for distributed learning in IoT environments.<n>A unified threat model is introduced that captures model inversion, membership inference, gradient leakage, and communication-based attacks.<n>The results highlight a fundamental trade-off between privacy strength and system efficiency.
  arXiv  Detail & Related papers  (2026-05-10T00:22:11Z)
• Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap [6.178815764761489]
  The rapid proliferation of the Internet of Things has intensified demand for robust privacy-preserving machine learning mechanisms.<n>This survey presents a comprehensive IoT-centric, cross-paradigm analysis of privacy-preserving machine learning.
  arXiv  Detail & Related papers  (2026-03-13T20:19:18Z)
• Secure Semantic Communications via AI Defenses: Fundamentals, Solutions, and Future Directions [44.71660423560587]
  This survey provides a defense-centered and system-oriented synthesis of security in SemCom via AI defense.<n>We present a structured taxonomy of defense strategies organized by where semantic integrity can be compromised in SemCom systems.<n>We also examine security utility operating envelopes that capture tradeoffs among semantic fidelity, robustness, latency, and energy.
  arXiv  Detail & Related papers  (2026-02-25T17:28:07Z)
• Adaptive Cybersecurity Architecture for Digital Product Ecosystems Using Agentic AI [0.0]
  This study introduces autonomous goal driven agents capable of dynamic learning and context-aware decision making.<n> Behavioral baselining, decentralized risk scoring, and federated threat intelligence sharing are important features.<n>The architecture provides an intelligent and scalable blueprint for safeguarding complex digital infrastructure.
  arXiv  Detail & Related papers  (2025-09-25T00:43:53Z)
• A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks [49.90474228895655]
  Cloud-edge-terminal collaborative intelligence (CETCI) is a fundamental paradigm within the artificial intelligence of things (AIoT) community.<n>CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems.<n>This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners.
  arXiv  Detail & Related papers  (2025-08-26T08:38:01Z)
• Towards Safety and Security Testing of Cyberphysical Power Systems by Shape Validation [42.350737545269105]
  complexity of cyberphysical power systems leads to larger attack surfaces to be exploited by malicious actors.<n>We propose to meet those risks with a declarative approach to describe cyber power systems and automatically evaluate security and safety controls.
  arXiv  Detail & Related papers  (2025-06-14T12:07:44Z)
• Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things [61.43014629640404]
  Zero-Trust Foundation Models (ZTFMs) embed zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems.<n>ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments.
  arXiv  Detail & Related papers  (2025-05-26T06:44:31Z)
• Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities [0.0]
  Traditional centralized security methods often struggle to balance privacy preservation and real-time threat detection in IoT networks.<n>This study proposes a Federated Learning-Driven Cybersecurity Framework designed specifically for IoT environments.<n>Secure aggregation of locally trained models is achieved using homomorphic encryption, allowing collaborative learning without exposing sensitive information.
  arXiv  Detail & Related papers  (2025-02-14T23:11:51Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• Blockchained Federated Learning for Threat Defense [0.0]
  This research paper introduces the development of an intelligent Threat Defense system, employing Federated Learning. The proposed framework combines Federated Learning for the distributed and continuously validated learning of the tracing algorithms. The aim of the proposed Framework is to intelligently classify smart cities networks traffic derived from Industrial IoT (IIoT) by Deep Content Inspection (DCI) methods.
  arXiv  Detail & Related papers  (2021-02-25T09:16:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.