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
arXiv Machine Learning
Sep 4

Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification

The paper investigates whether passive motion traces recorded during selfie capture can serve as an auxiliary signal for detecting spoofing and verifying users in mobile remote identity verification systems. It introduces the CanSelfie dataset, comprising 375 multi‑sensor sequences from 30 participants, and evaluates seven time‑series classifiers and eight anomaly detectors across various sensor configurations. Results show that accelerometer‑only classifiers achieve very low false rejection rates, while certain models achieve low false acceptance rates and high verification accuracy, indicating that selfie‑capture motion is a promising low‑friction evidence channel.

By Erkka Rantahalvari, Olli Silv\'en, Zinelabidine Boulkenafet, Constantino \'Alvarez Casado
arXiv Statistics ML
Sep 10

Accountable and uncertainty-aware evaluation of sensor-based AI under distribution shift: devices, subjects, and nearly three years underground

arXiv:2609.09257v1 Announce Type: cross Abstract: Sensor-based AI systems are rarely operated under the conditions under which they were trained: devices, personnel and recording epochs change, and e...

By Benny Platte (Mittweida University of Applied Sciences), Rico Thomanek (Mittweida University of Applied Sciences), Christian Roschke (Mittweida University of Applied Sciences), Marc Ritter (Mittweida University of Applied Sciences)
arXiv Machine Learning
2d ago

FGSM Attack Transferability from Simulation to Quantum Hardware in Network Intrusion Detection

The study investigates the vulnerability of a hybrid variational quantum classifier (VQC) used for network intrusion detection to adversarial attacks. By training on the Friday DDoS subset of CICIDS2017, the authors evaluate label‑flipping during training and FGSM evasion at prediction time, comparing results in simulation and on IBM’s Fez superconducting processor (Heron r2). They find that models with similar accuracy can differ markedly in attack resilience: a low‑confidence model loses half its detections to minor input changes, whereas a high‑confidence model requires larger perturbations to fail. Importantly, attacks crafted in simulation transfer to real hardware, and simulated device noise reduces confidence—making attacks easier—without lowering accuracy, indicating that accuracy alone does not reveal a model’s exposure.

By Zeeshan Ajmal, Kimmo Halunen, Marc Mau{\ss}ner, Volker Reers, Arif Ali Khan