Search

The increase in popularity of wireless networks in industrial, embedded, medical and public sectors has made them an appealing attack surface for attackers who exploit the vulnerabilities in network protocols to launch attacks such as Evil Twin, Man-in-the-middle, sniffing, etc., which may result in economic and non-economic losses. To protect wireless networks against such attacks, IEEE 802.11 keep updating the protocol standards with new and more secure versions. There has always been a direct correlation between attacks and the improvement of protocol standards. As the sophistication of attacks increases, protocol standards tend to move towards higher security, resulting in a significant rise in both latency and computational overhead, and severe degradation in the performance of low-latency applications such as Industrial Internet of Things (IIoT), automotive, robotics, etc. In this paper, we make an attempt to highlight the importance of both latency and security in wireless networks from implementation and performance perspective. We make a review of existing IEEE 802.11 protocols in terms of security offered and overhead incurred to substantiate the fact that there is a need of a protocol which in addition to providing optimum security against attacks also maintains the latency and overhead. We also propose a secure and low-latency protocol known as Secure Authentication Protocol (SAP) which operates in two phases - registration and authentication, where the first phase is a one time process implemented using asymmetric cryptography and the second phase is implemented using symmetric cryptography. The protocol is structured in a way that it maintains the original structure of IEEE 802.11 protocols and performs both phases using fewer messages than existing protocols. By simulating the protocol using well-established OMNeT++ simulator, we proved that the proposed protocol incurs a low computation overhead, making it ideal for low-latency applications. We extensively verified the security properties of the proposed protocol using formal verification through widely-accepted Scyther tool. Finally, we perform a comparative analysis of SAP with existing IEEE 802.11 wireless network protocols to highlight the improvement.

Defect detection on rail lines is essential for ensuring safe and efficient transportation. Current image analysis methods with deep neural networks (DNNs) for defect detection often focus on the defects themselves while ignoring the related context. In this work, we propose a fusion model that combines both a targeted defect search and a context analysis, which is seen as a multimodal fusion task. Our model performs rule-based decision-level fusion, merging the confidence scores of multiple individual models to classify rail-line defects. We call the model “hybrid” in the sense that it is composed of supervised learning components and rule-based fusion. We first propose an improvement to existing vision-based defect detection methods by incorporating a convolutional block attention module (CBAM) in the you only look once (YOLO) versions 5 (YOLOv5) and 8 (YOLOv8) architectures for the detection of defects and contextual image elements. This attention module is applied at different detection scales. The domain-knowledge rules are applied to fuse the detection results. Our method demonstrates improvements over baseline models in vision-based defect detection. The model is open for the integration of modalities other than an image, e.g., sound and accelerometer data.

Background: Dairy farmworkers are exposed to a variety of respiratory hazards, including organic and inorganic dust, allergens, disinfectants, and gases emitted by cows and their wastes resulting in a range of adverse health effects. In Egypt, large herd dairy farms (>1,000 cattle) are growing in both size and number and thereby more workers are employed. However, there is a lack of studies on the respiratory health status of these workers. Accordingly, the present study aimed to determine the prevalence of respiratory problems, assess ventilatory functions, and highlight the predictors of abnormal spirometry patterns among Egyptian dairy farmworkers. Methods: A cross-sectional study was carried out on 282 male workers, of whom 141 were dairy farmworkers and the other 141, not involved in livestock handling, were enrolled as controls. Full history, clinical examination, and ventilatory function measurements were done for both groups. Results: Dairy farmworkers had a significantly higher prevalence of respiratory symptoms (throat irritation and/or sore throat, cough, sputum production, and difficulty breathing) than controls as well as bronchitis, wheezes on chest auscultation, and obstructive ventilatory patterns. Older age (>37 years), longer smoking duration (>10 years), and longer working duration (>4 years) were independent predictors of abnormal spirometry patterns, particularly obstructive patterns, in dairy farmworkers. Conclusions: Large herd dairy farms, despite being open and naturally ventilated, are hazardous to workers' respiratory health. Hence, the provision of personal protective equipment, periodic spirometry examinations as well as mandatory breaks and days off, are highly urged. [ABSTRACT FROM AUTHOR]