On the cybersecurity of LoRaWAN-based system: a Smart-Lighting case study
- URL: http://arxiv.org/abs/2510.20494v1
- Date: Thu, 23 Oct 2025 12:36:23 GMT
- Title: On the cybersecurity of LoRaWAN-based system: a Smart-Lighting case study
- Abstract summary: This paper presents an experimental investigation of architectural configurations for a LoRaWAN-based Smart-Lighting project.<n>We assess the system's robustness in a series of iterative experiments conducted both in-vitro and on-site.<n>The results show that most attacks on a LoRaWAN network are unsuccessful, also highlighting unresolved issues with the installed products.
- Score: 0.4804905638984918
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cyber-physical systems and the Internet of Things (IoT) are key technologies in the Industry 4.0 vision. They incorporate sensors and actuators to interact with the physical environment. However, when creating and interconnecting components to form a heterogeneous smart systems architecture, these face challenges in cybersecurity. This paper presents an experimental investigation of architectural configurations for a LoRaWAN-based Smart-Lighting project, aimed at verifying and improving the system's robustness against attacks. We assess the system's robustness in a series of iterative experiments conducted both in-vitro and on-site. The results show that most attacks on a LoRaWAN network are unsuccessful, also highlighting unresolved issues with the installed products. The most successful attacks are high-power jamming attacks within a few meters of the target, which, in the case of gateways, can be mitigated through gateway redundancy.
Related papers
- 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) - Enabling Security on the Edge: A CHERI Compartmentalized Network Stack [42.78181795494584]
CHERI provides strong security from the hardware level by enabling fine-grained compartmentalization and memory protection.<n>Our case study examines the trade-offs of isolating applications, TCP/IP libraries, and network drivers on a CheriBSD system deployed on the Arm Morello platform.
arXiv Detail & Related papers (2025-07-07T09:37:59Z) - CyFence: Securing Cyber-Physical Controllers via Trusted Execution Environment [45.86654759872101]
Cyber-physical systems (CPSs) have experienced a significant technological evolution and increased connectivity, at the cost of greater exposure to cyber-attacks.<n>We propose CyFence, a novel architecture that improves the resilience of closed-loop control systems against cyber-attacks by adding a semantic check.<n>We evaluate CyFence considering a real-world application, consisting of an active braking digital controller, demonstrating that it can mitigate different types of attacks with a negligible overhead.
arXiv Detail & Related papers (2025-06-12T12:22:45Z) - Intelligent IoT Attack Detection Design via ODLLM with Feature Ranking-based Knowledge Base [0.964942474860411]
Internet of Things (IoT) devices have introduced significant cybersecurity challenges.<n>Traditional machine learning (ML) techniques often fall short in detecting such attacks due to the complexity of blended and evolving patterns.<n>We propose a novel framework leveraging On-Device Large Language Models (ODLLMs) augmented with fine-tuning and knowledge base (KB) integration for intelligent IoT network attack detection.
arXiv Detail & Related papers (2025-03-27T16:41:57Z) - Enhancing Network Security Management in Water Systems using FM-based Attack Attribution [43.48086726793515]
We propose a novel model-agnostic Factorization Machines (FM)-based approach that capitalizes on water system sensor-actuator interactions to provide granular explanations and attributions for cyber attacks.<n>In multi-feature cyber attack scenarios involving intricate sensor-actuator interactions, our FM-based attack attribution method effectively ranks attack root causes, achieving approximately 20% average improvement over SHAP and LEMNA.
arXiv Detail & Related papers (2025-03-03T06:52:00Z) - Modern DDoS Threats and Countermeasures: Insights into Emerging Attacks and Detection Strategies [49.57278643040602]
Distributed Denial of Service (DDoS) attacks persist as significant threats to online services and infrastructure.<n>This paper offers a comprehensive survey of emerging DDoS attacks and detection strategies over the past decade.
arXiv Detail & Related papers (2025-02-27T11:22:25Z) - Device-aware Optical Adversarial Attack for a Portable Projector-camera System [45.58483539606022]
Deep-learning-based face recognition systems are susceptible to adversarial examples in both digital and physical domains.<n>This paper addresses the limitations of existing projector-camera-based adversarial light attacks in practical FR setups.<n>By incorporating device-aware adaptations into the digital attack algorithm, we mitigate the degradation from digital to physical domains.
arXiv Detail & Related papers (2025-01-23T13:55:23Z) - Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics [68.36528819227641]
This paper systematically evaluates the robustness of Vision-Language-Action (VLA) models.<n>We introduce two untargeted attack objectives that leverage spatial foundations to destabilize robotic actions, and a targeted attack objective that manipulates the robotic trajectory.<n>We design an adversarial patch generation approach that places a small, colorful patch within the camera's view, effectively executing the attack in both digital and physical environments.
arXiv Detail & Related papers (2024-11-18T01:52:20Z) - FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks.
We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness.
Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
arXiv Detail & Related papers (2024-03-26T08:51:23Z) - Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS)
FLEKD enables a more flexible aggregation method than conventional model fusion techniques.
Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
arXiv Detail & Related papers (2024-01-22T14:16:37Z) - Artificial Intelligence-Based Smart Grid Vulnerabilities and Potential
Solutions for Fake-Normal Attacks: A Short Review [0.0]
Smart grid systems are critical to the power industry, however their sophisticated architectural design and operations expose them to a number of cybersecurity threats.
Artificial Intelligence (AI)-based technologies are becoming increasingly popular for detecting cyber assaults in a variety of computer settings.
The present AI systems are being exposed and vanquished because of the recent emergence of sophisticated adversarial systems such as Generative Adversarial Networks (GAN)
arXiv Detail & Related papers (2022-02-14T21:41:36Z) - Intermittent Jamming against Telemetry and Telecommand of Satellite
Systems and A Learning-driven Detection Strategy [1.4620086904601468]
A security deficiency in the physical layer is addressed with a conceptual framework, considering the cyber-physical nature of the satellite systems.
A learning-driven detection scheme is proposed, and the lightweight convolutional neural network (CNN) is designed.
The results show that deficiency attacks against the satellite systems can be detected by employing the proposed scheme.
arXiv Detail & Related papers (2021-07-10T17:04:22Z) - Machine Learning in Generation, Detection, and Mitigation of
Cyberattacks in Smart Grid: A Survey [1.3299946892361474]
Smart grid (SG) is a complex cyber-physical system that utilizes modern cyber and physical equipment to run at an optimal operating point.
Cyberattacks are the principal threats confronting the usage and advancement of the state-of-the-art systems.
Machine learning (ML) is being used to exploit and defend the cyberattacks in SG by the attackers and system operators.
arXiv Detail & Related papers (2020-09-01T05:16:51Z) - Measurement-driven Security Analysis of Imperceptible Impersonation
Attacks [54.727945432381716]
We study the exploitability of Deep Neural Network-based Face Recognition systems.
We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim.
We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
arXiv Detail & Related papers (2020-08-26T19:27:27Z) - Deep Learning-Based Intrusion Detection System for Advanced Metering
Infrastructure [0.0]
The smart grid is exposed to a wide variety of threats that could be translated into cyber-attacks.
In this paper, we develop a deep learning-based intrusion detection system to defend against cyber-attacks.
arXiv Detail & Related papers (2019-12-31T21:06:20Z)
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.