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arXiv AI

Physics-Constrained Digital Twins for Sensor Integrity in Urban Pedestrian Flow: Detecting Stealthy False Data Injection with Conformal Guarantees

The paper introduces physics‑constrained digital twins for urban pedestrian flow that detect stealthy false data injection attacks. By estimating directed flows on a street graph, assimilating counts with a learned graph‑localized gain, and training against a flow‑conservation residual, the twin combines innovation and residuals for detection. Adaptive conformal calibration sets alarm thresholds, and the authors quantify the attack margin—showing a 0.54 reduction in worst‑case corruption for a single compromised device and 0.19 when a third of the fleet is compromised, highlighting the benefit of conservation laws over mere locality.

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arXiv Machine Learning
Sep 4

Privacy Leakage in Federated Learning: Gradient-Based Client Identity Inference and Defenses for Inertial Sensing in Vehicular Edge Networks

The paper investigates how federated learning (FL) updates in vehicular edge networks can reveal client identities through gradient-based attacks on inertial sensor data, using the UCI Human Activity Recognition benchmark as a proxy. Experiments show that an honest-but-curious server can identify clients with near-perfect accuracy from unprotected updates. The authors evaluate lightweight defenses—clipping followed by Gaussian noise and ensemble FL—to mitigate this privacy risk while preserving model utility, reporting differential‑privacy budgets and empirical results across multiple attack classifiers and data partitions.

By Ali Akarma (Islamic University of Madinah, Madinah, Saudi Arabia, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia), Toqeer Ali Syed (Islamic University of Madinah, Madinah, Saudi Arabia), Muhammad Khan (University of the West of England, Bristol, U.K), Qurat-ul-ain Mastoi (University of the West of England, Bristol, U.K), Adeel Ahmad (Islamic University of Madinah, Madinah, Saudi Arabia)
benchmarkssafety
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arXiv AI
Jun 3

FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems

arXiv:2606. 03430v1 Announce Type: cross Abstract: Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) deployed in energy infrastructure are vulnerable to model theft attacks, which allow adversaries to create evasive traffic offline.

By Maxime Schwarzer, Laurin Holz, Tobias Huerten, Johannes Loevenich, Thies Moehlenhof, Roberto Rigolin F. Lopes, Veit Hagenmeyer
diffusion
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arXiv Machine Learning
Jul 10

Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning

arXiv:2607. 08137v1 Announce Type: cross Abstract: Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in the decision-making process within dynamic vehicular environments.

By Zifan Zhang, Minghong Fang, Dianwei Chen, Zhuqing Liu, Prashant Khanduri, Xianfeng Yang, Anupam Das, Yuchen Liu
agentsreinforcement-learningroboticssafety
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arXiv Machine Learning
Sep 7

Privacy Failure in Split-LLM Training, The Returned Gradient Nullifies the Decoys

The paper reports a privacy breach in a two-node split‑LLM training system where the returned gradient reveals which data rows were real, despite the system passing standard privacy checks. By exploiting the fact that decoy rows produce zero gradients, an attacker can identify real rows with 100% accuracy across multiple runs. The authors demonstrate that adding gradient clipping and noise can mitigate the leak, but the system remains vulnerable to several untested attack vectors.

By Georgios Politis, Evangelos Pappas
llmssafety
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arXiv AI
Sep 7

AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applications

The paper outlines methods and applications for creating AI‑powered digital twins (DTs) tailored to urban traffic management. It emphasizes that while most DT research focuses on sensing and perception, the true differentiator lies in the DT’s predictive and decision‑making "brain" that extracts patterns and informs actions. By integrating artificial intelligence with low‑latency, high‑bandwidth cyber‑physical systems, the authors propose a framework that can guide researchers and practitioners in addressing challenges, fostering interdisciplinary dialogue, and unlocking diverse urban transportation applications.

By Yongjie Fu, Mehmet K. Turkcan, Mahshid Ghasemi, Zhaobin Mo, Chengbo Zang, Abhishek Adhikari, Zoran Kostic, Gil Zussman, Xuan Di
computer-vision
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