In Numeris Veritas: An Empirical Measurement of Wi-Fi Integration in Industry
- URL: http://arxiv.org/abs/2509.16987v1
- Date: Sun, 21 Sep 2025 09:01:58 GMT
- Title: In Numeris Veritas: An Empirical Measurement of Wi-Fi Integration in Industry
- Authors: Vyron Kampourakis, Christos Smiliotopoulos, Vasileios Gkioulos, Sokratis Katsikas,
- Abstract summary: A critical knowledge gap exists regarding the prevalence and security configuration of Wi-Fi in real-world settings.<n>We create the first publicly available dataset of 1,087 high-confidence industrial Wi-Fi networks.<n>Our findings reveal a growing adoption of Wi-Fi across industrial sectors but underscore alarming security deficiencies.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Traditional air gaps in industrial systems are disappearing as IT technologies permeate the OT domain, accelerating the integration of wireless solutions like Wi-Fi. Next-generation Wi-Fi standards (IEEE 802.11ax/be) meet performance demands for industrial use cases, yet their introduction raises significant security concerns. A critical knowledge gap exists regarding the empirical prevalence and security configuration of Wi-Fi in real-world industrial settings. This work addresses this by mining the global crowdsourced WiGLE database to provide a data-driven understanding. We create the first publicly available dataset of 1,087 high-confidence industrial Wi-Fi networks, examining key attributes such as SSID patterns, encryption methods, vendor types, and global distribution. Our findings reveal a growing adoption of Wi-Fi across industrial sectors but underscore alarming security deficiencies, including the continued use of weak or outdated security configurations that directly expose critical infrastructure. This research serves as a pivotal reference point, offering both a unique dataset and practical insights to guide future investigations into wireless security within industrial environments.
Related papers
- Contrastive Learning for Privacy Enhancements in Industrial Internet of Things [5.670812806008398]
The Industrial Internet of Things (IIoT) integrates intelligent sensing, communication, and analytics into industrial environments.<n>IIoT introduces significant privacy and confidentiality risks due to the sensitivity of operational data.<n> Contrastive learning has emerged as a promising approach for privacy-preserving analytics by reducing reliance on labeled data and raw data sharing.
arXiv Detail & Related papers (2026-01-31T05:11:57Z) - Adversary-Aware Private Inference over Wireless Channels [51.93574339176914]
AI-based sensing at wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications.<n>As sensitive personal data can be reconstructed by an adversary, transformation of the features are required to reduce the risk of privacy violations.<n>We propose a novel framework for privacy-preserving AI-based sensing, where devices apply transformations of extracted features before transmission to a model server.
arXiv Detail & Related papers (2025-10-23T13:02:14Z) - Large AI Model-Enabled Secure Communications in Low-Altitude Wireless Networks: Concepts, Perspectives and Case Study [92.15255222408636]
Low-altitude wireless networks (LAWNs) have the potential to revolutionize communications by supporting a range of applications.<n>We investigate some large artificial intelligence model (LAM)-enabled solutions for secure communications in LAWNs.<n>To demonstrate the practical benefits of LAMs for secure communications in LAWNs, we propose a novel LAM-based optimization framework.
arXiv Detail & Related papers (2025-08-01T01:53:58Z) - A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects [25.772082334809678]
Wi-Fi sensing has emerged as a powerful non-intrusive technology for recognizing human activities, monitoring vital signs, and enabling context-aware applications.<n>However, the performance of Wi-Fi sensing often degrades when applied to new users, devices, or environments due to significant domain shifts.<n>To address this challenge, researchers have proposed a wide range of generalization techniques aimed at enhancing the robustness and adaptability of Wi-Fi sensing systems.
arXiv Detail & Related papers (2025-03-11T03:18:20Z) - ACRIC: Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
Recent security incidents in safety-critical industries exposed how the lack of proper message authentication enables attackers to inject malicious commands or alter system behavior.<n>These shortcomings have prompted new regulations that emphasize the pressing need to strengthen cybersecurity.<n>We introduce ACRIC, a message authentication solution to secure legacy industrial communications.
arXiv Detail & Related papers (2024-11-21T18:26:05Z) - High-Security Hardware Module with PUF and Hybrid Cryptography for Data Security [1.8434042562191815]
This research highlights the rapid development of technology in the industry, particularly Industry 4.0.
Despite providing efficiency, these developments also bring negative impacts, such as increased cyber-attacks.
This research proposes a solution by developing a hardware security module (HSM) using a field-programmable gate array (FPGA) with physical unclonable function (PUF) authentication and a hybrid encryption data security system.
arXiv Detail & Related papers (2024-09-16T02:06:49Z) - Secure Integration of 5G in Industrial Networks: State of the Art, Challenges and Opportunities [2.479074862022315]
We describe the state-of-the-art and derive recommendations for the secure integration of 5G into industrial networks.<n>We identify opportunities to utilize 5G to enhance security and indicate remaining challenges.
arXiv Detail & Related papers (2024-08-29T18:00:17Z) - Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future
Directions [78.5371215066019]
Over-the-air federated learning (OTA-FL) enables users at the network edge to share spectrum resources and achieves efficient and low-latency global model aggregation.
This paper provides a holistic review of progress in OTA-FL and points to potential future research directions.
arXiv Detail & Related papers (2023-07-03T12:44:52Z) - Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing
Capabilities and Limitations [16.819111460629397]
This work aims to shed light on the impact of Wi-Fi 6 features on the sensing performance and to create a benchmark for future research on Wi-Fi sensing.
We perform an extensive CSI data collection campaign involving 3 individuals, 3 environments, and 12 activities, using Wi-Fi 6 signals.
An anonymized ground truth obtained through video recording accompanies our 80-GB dataset, which contains almost two hours of CSI data from three collectors.
arXiv Detail & Related papers (2023-02-02T10:21:00Z) - WiFi-based Spatiotemporal Human Action Perception [53.41825941088989]
An end-to-end WiFi signal neural network (SNN) is proposed to enable WiFi-only sensing in both line-of-sight and non-line-of-sight scenarios.
Especially, the 3D convolution module is able to explore thetemporal continuity of WiFi signals, and the feature self-attention module can explicitly maintain dominant features.
arXiv Detail & Related papers (2022-06-20T16:03:45Z) - Data Heterogeneity-Robust Federated Learning via Group Client Selection
in Industrial IoT [57.67687126339891]
FedGS is a hierarchical cloud-edge-end FL framework for 5G empowered industries.
Taking advantage of naturally clustered factory devices, FedGS uses a gradient-based binary permutation algorithm.
Experiments show that FedGS improves accuracy by 3.5% and reduces training rounds by 59% on average.
arXiv Detail & Related papers (2022-02-03T10:48:17Z) - Federated Learning for Industrial Internet of Things in Future
Industries [106.13524161081355]
The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems.
Recently, artificial intelligence (AI) has been widely utilized for realizing intelligent IIoT applications.
Federated Learning (FL) is particularly attractive for intelligent IIoT networks by coordinating multiple IIoT devices and machines to perform AI training at the network edge.
arXiv Detail & Related papers (2021-05-31T01:02:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.