Network Foundation Models
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Related Articles from SNS
Towards Intrusion Detection Systems for RPL-based IoT Networks using Foundation Models
Announce Type: new Abstract: AI-based intrusion detection systems (IDS) have shown promise in detecting attacks on IoT systems. In this work, we explore the use of foundation models to detect and identify attacks, with a specific focus on RPL-based IoT networks. We study multiple attack types, attack variations, and network configurations, and provide insights into the performance of foundation models for attack identification.
Federated Foundation Models over Vehicular Networks
arXiv:2606.06786v1 Announce Type: new Abstract: This paper presents a forward-looking vision for integrating the emerging multi-modal multi-task federated foundation models (M3T FedFMs) into vehicular networks, with the goal of unifying the expressive power of multi-modal multi-task foundation models (M3T FMs) with the privacy-preserving and distributed learning capabilities of federated learning (FL). Given the largely underexplored nature of this research direction, we first introduce the...
Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks
Announce Type: new Abstract: Network dynamics - including spreading, influence maximisation, and epidemic modelling - remain largely confined to the transductive paradigm, where models are trained on a single network and cannot be reused on unseen graphs without retraining. We argue that inductive cross-network generalisation is a necessary prerequisite for Graph Foundation Models (GFMs) in this domain and propose four design properties towards this goal. As a proof of concept, ts-net...
Enhancing the Socioeconomic Understanding of Foundation Models with Urban Mobility
Announce Type: new Abstract: Foundation models have recently been applied to urban socioeconomic prediction using POI text, satellite imagery, and geospatial descriptions. However, these models mostly rely on static attributes of individual places, while ignoring the mobility patterns that reveal how places are functionally connected. To address this gap, we explore whether mobility networks can elicit the geospatial capabilities of foundation models by explicitly encoding connectivity among...
On the Uncertainty Quantification Ability of Tabular Foundation Models
Announce Type: cross Abstract: Foundation models (FMs) have achieved substantial success in generalizing across tasks without problemspecific training or fine-tuning. However, many critical applications in mechanics and computational science require not only accurate predictions but also reliable uncertainty quantification (UQ). Herein we investigate the UQ capabilities of tabular FMs in regression tasks through a comprehensive empirical study comparing Tabular Prior-Data Fitted Networks...
NetVAD: Foundation-Model Representation Learning for Identifier-Free Unsupervised Intrusion Detection
Announce Type: new Abstract: Detecting zero-day exploits in production networks requires robust Intrusion Detection Systems (IDS). However, current unsupervised models struggle to match the performance of supervised classifiers, which are trained for specific attacks only. To bridge this gap, we leverage the emerging capabilities of Network Foundation Models.
AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model
arXiv:2507.08920v4 Announce Type: replace-cross Abstract: We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, encompassing pretraining scaling laws, emergent capability analysis, in-context learning mechanism, and test-time scaling algorithm. To guarantee robust scalability, we establish a predictive scaling law and reveal the progressive emergence of structural understanding via loss perspective,...
SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction
Announce Type: cross Abstract: This paper proposes SpikeWFM, a novel hybrid architecture that integrates spiking neural networks (SNNs) with conventional artificial neural network (ANN)-based transformers for wireless foundation models (WFMs). Inspired by the noise-robust and energy-efficient information processing in the human brain, SpikeWFM aims to enhance the resilience of WFMs against noise and interference while maintaining strong generalization capabilities across diverse wireless...
When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
Announce Type: replace Abstract: Tabular foundation models based on pretrained prior-data fitted networks~(PFNs) have shown strong generalization on diverse tabular tasks, but they are typically designed for \emph{non-strategic} settings where data distributions are independent of deployed classifiers. In many real-world decision scenarios, however, individuals may strategically modify their features after deployment to obtain favorable outcomes, inducing a post-deployment distribution...
SurvPFN: Towards Foundation Models for Survival Predictions
Announce Type: new Abstract: Tabular foundation models (TFMs) have made rapid progress in standard classification and regression, but time-to-event survival prediction tasks have remained largely untouched. Unlike in standard regression tasks, survival prediction models must account for censored data. Standard TFMs cannot handle natively censored data, leading to biased and inaccurate predictions, making them unsuitable for real-world applications.