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 Cogniti
The proliferation of distributed systems and sophisticated cyber threats necessitates advanced, privacy-preserving security solutions capable of operating at the edge.
This development addresses critical vulnerabilities in distributed infrastructure by introducing explainable, federated AI for threat detection, enhancing resilience and trust in complex digital environments.
Security paradigms are shifting from centralized, opaque models to distributed, transparent, and privacy-preserving AI-driven analytics, enabling more adaptive and scalable threat intelligence.
- · Cloud providers
- · IoT device manufacturers
- · Cybersecurity firms
- · Critical infrastructure operators
- · Traditional centralized security vendors
- · Attackers targeting distributed systems
Enhanced security and resilience of distributed infrastructure systems against evolving cyber threats.
Increased adoption of federated learning and explainable AI in enterprise security architectures due to proven efficacy and privacy benefits.
The establishment of new industry standards and regulatory frameworks around explainable and distributed AI for cybersecurity.
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Read at arXiv cs.AI