TL;DR
Experimental results show that V5GIDS achieves strong detection performance in the evaluated settings while improving deployment relevance through orchestration alignment, reduced telemetry exchange, and resource-aware distributed operation.
Results show that GridCAD-LLM provides a resilient, explainable, and scalable foundation for securing DNP3-based smart-grid communications.
The rapid integration of Network Function Virtualization (NFV) in 5G-Advanced and 6G infrastructures has introduced significant security vulnerabilities, most notably in Distributed Denial-of-Service (DDoS) attacks. Modern DDoS threats have evolved into sophisticated multi- vector campaigns that leverage AI to bypass t...
The disintegration of the traditional network perimeter, precipitated by the accelerated adoption of hybrid cloud architectures and the proliferation of remote work, has necessitated a fundamental shift in cybersecurity strategy. This research presents an API-centric framework for security orchestration, grounded in th...
The framework is presented as a bounded, server-assisted robustness-oriented training strategy for heterogeneous IoT/edge intrusion detection, and shows competitive primary performance and stronger robustness in several severe label-skew settings.
Open-RAN is transforming 5G and Beyond-5G networks by promoting openness, flexibility, and interoperability across network components. However, this architectural shift introduces new security challenges due to expanded attack surfaces, distributed control mechanisms, and exposed interfaces. Traditional Intrusion Detec...
An AI-assisted, cross-layer security orchestration framework that integrates epoch-wise telemetry with ML-based risk estimation and formalizes mitigation as a Constrained Markov Decision Process (CMDP), and empirical evidence that adaptive mitigation can reduce security risk without sacrificing service guarantees is pr...
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI.
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