arXiv AI August 21, 2026 By Phai Vu Dinh, Diep N. Nguyen, Dinh Thai Hoang, Marwan Krunz, Quang Uy Nguyen, Son Pham Bao, Eryk Dutkiewicz

Teacher-free Latent Self-distillation and Class-separable Representations for Lightweight IoT Attack Detection

Read the original on arXiv AI →

arXiv:2403. 15509v3 Announce Type: replace-cross Abstract: Knowledge distillation (KD) has been widely used to improve lightweight AI models by transferring soft-label knowledge from a large teacher model to a student model.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

efficiency

arXiv Machine Learning

Jul 9

What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale

arXiv:2510. 13817v2 Announce Type: replace Abstract: The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountability.

By Rameen Mahmood, Tousif Ahmed, Sai Teja Peddinti, Danny Yuxing Huang

llms robotics efficiency safety

arXiv Machine Learning

Sep 25

Unmasking Shortcut Learning in IoT Intrusion Detection: A Forensic, Multi-Paradigm Evaluation of Feature Dependence and Data Leakage

The paper investigates whether machine learning models for IoT intrusion detection truly learn attack patterns or rely on dataset shortcuts. Using the CyberFlowIoT-GICAP benchmark, the authors evaluate four learning paradigms across different feature sets and split strategies, finding that performance is largely driven by feature representation and that tree-based models can exploit temporal artifacts. The study also highlights asymmetric attack detectability and proposes a four-point protocol checklist for realistic evaluation.

By Uday Shankar Roy, Mahbuba Jahan Minu

benchmarks

arXiv Machine Learning

Jun 30

A Novel Latent-Class Attack and its Detection by Class Subspace Orthogonalization

arXiv:2606. 29112v1 Announce Type: new Abstract: Deep learning, which in general relies on voluminous amounts of training data, is vulnerable to data poisoning attacks, including error-generic attacks and backdoors (Trojans).

By Guangmingmei Yang, David J. Miller, George Kesidis

computer-vision

arXiv Machine Learning

Sep 22

An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networks

arXiv:2609.23097v1 Announce Type: cross Abstract: Internet of Things (IoT) systems are increasingly deployed in smart homes, transportation, energy systems, and critical infrastructure. This broad co...

By Li Yang

llms

arXiv Machine Learning

Aug 4

Deep Learning for Cyber Threat Detection and Mitigation in Healthcare-IoT

arXiv:2608. 00118v1 Announce Type: cross Abstract: Cybersecurity is a fundamental requirement for protecting wearable devices used in healthcare Internet of Things (H-IoT) systems.

By Mirza Akhi

efficiency safety

arXiv AI

Jul 13

Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation

arXiv:2603. 17717v4 Announce Type: replace-cross Abstract: Supervised detection of network attacks has always been a critical part of network intrusion detection systems (NIDS).

By Iakovos-Christos Zarkadis, Christos Douligeris

reinforcement-learning multimodal safety

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