cs.NI · 2026-07-29 · No. 68

Networking and Internet Architecture, 2026-07-29.

3 new papers in cs.NI. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    Untangling Co-Drift: Proactive Multi-Intent Failure Prediction and Root-Cause Disambiguation for Self-Driving Networks

    Md. Kamrul Hossain, Walid Aljoby

    cs.NI · cs.LG · cs.RO

    The vision of self-driving networks that monitor, reason, and act upon themselves with minimal human intervention relies on tightly coupled monitoring, analytics, and actuation functions. In this work, we treat these functions as three operational macro-intents: continuous telemetry, real-time analytics, and programmatic actuation, and formalize the health of each function as an intent that the network must continuously satisfy. A critical,...

    arxiv.org/abs/2607.25989 · PDF

  2. 02

    WALoMA: A Multitask Wireless Foundation Model via Adaptive Low-Rank Masked Autoencoders

    Madi Makin, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil

    cs.NI · cs.LG

    This paper proposes a multitask wireless foundation model via adaptive low-rank masked autoencoders (WALoMA), a unified multi-task foundation model for sixth-generation (6G) wireless physical layer architectures, to address the limitations of specialized, task-specific deep learning models and the practical challenge of scarce labeled wireless datasets. By leveraging concepts inspired by foundation models, the proposed framework adopts a...

    arxiv.org/abs/2607.25763 · PDF

  3. 03

    Robust Unsupervised Network Intrusion Detection via Federated Learning with Selective Aggregation under Anomalous Sample Contamination

    Shohei Kamiguchi, Takayuki Nishio

    cs.NI · cs.LG

    Network intrusion detection systems (NIDS) have become essential for Internet of Things (IoT) environments, as malware targeting IoT devices continues to evolve in sophistication. Unsupervised learning approaches offer a promising direction by removing the dependency on labeled datasets. However, the common assumption that training data are entirely clean is often violated in practice, particularly when data samples are collected directly...

    arxiv.org/abs/2607.25439 · PDF

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