Safety Vulnerability Research Articles

Artificial intelligence driven cyberattack detection system using integration of deep belief network with convolution neural network on industrial IoT

In Industry 4.0, information and communication technology (ICT) was employed in numerous significant infrastructures, like financial networks, smart factories, and power plants, to automate and certify industrial systems. In power control systems, ICT technologies such as IIoT have improved automated monitoring, but legacy methods, originally autonomous, now connect with external networks. This progress has presented safety vulnerabilities from legacy ICT systems. Hence, various cybersecurity approaches are developed and examined to deal with cyberattacks and vulnerabilities. Utilizing new cybersecurity models in power control systems poses risks due to their uncertified safety. Ensuring their stability and efficiency is significant for maintaining reliable power delivery and incorporating these technologies into power control systems. Therefore, this study designs a Next–Generation Cybersecurity Attack Detection using an ensemble deep learning model (NGCAD-EDLM) technique in the IIoT environment. The main cause of the NGCAD-EDLM technique is the automatic recognition of cyber-attacks. In the NGCAD-EDLM approach, the primary data normalization phase utilizing min-max normalization is performed. Next, the honey-badger algorithm (HBA) approach selects the feature subsets. Furthermore, an ensemble deep learning (DL) of two methods, namely convolutional neural networks (CNNs) and deep belief networks (DBNs) methods, are employed for classification. In addition, the DL techniques' hyperparameter selection is accomplished using the lotus effect optimization algorithm (LEOA) method. A complete set of simulation validation is performed to establish the experimental analysis of the NGCAD-EDLM method. The performance validation of the NGCAD-EDLM method exhibited a superior accuracy value of 99.21 % over other existing techniques.

Secured intelligent transportation with privacy retention through blockchain framework

The Secured Independent Intelligent Transport System (SIITS) is poised to revolutionize traditional transport management systems, leveraging autonomous vehicles (AVs) connected through an open-channel Internet to link Traffic Command Centers (TCCs), Road Side Units (RSUs), and AVs within the SIITS framework. However, this reliance on the Internet exposes users to various security risks, safety vulnerabilities, and other challenges that impede the progress of SIITS applications. In this method, ensuring robust security management and trustworthiness is paramount for the long-term adoption of this innovative trend. While previous efforts have focused on integrating security methods from multiple disciplines into a unified reference design, this article presents a reference architecture primarily centered around ITS safety. Additionally, the article introduces a proposed framework for enhancing ITS safety, addressing the confidence issues. To further address these challenges, the article offers categorization of goods, Big Data methods and services, and validates the utility of ITS business analytics for corporate applications through a groundbreaking multi-tier ITS security architecture.

Safe Health-Care Environment Advisory Program Counseling Experience Sharing

<p>文以醫療機構環境與基礎建設各系統現場訪視，採系統追蹤方式，從受訪2家醫療院所環境風險管理與火災風險辨識著手，以消防安全設施設備管理；各樓棟防火區劃完整度檢視；避難逃生路徑規劃與逃生方向指引；醫用氣體使用安全；危險物質儲存、使用、洩漏管理；用電與各機房環境安全及緊急災害應變措施機制等面向，重點檢視受輔導醫療機構設施設備與環境安全現況及安全脆弱點，提供醫療機構管理階層，提升病人就醫環境整體安全之參考。</p> <p> </p><p>This paper describes a systematic tracking approach for conducting on-site inspections at two medical facilities and assessing their institutional environments and infrastructure. The assessment centers on the management of environmental and fire risks, including overseeing fire safety facilities and equipment. The comprehensiveness of the fire zoning of individual buildings and the planning of and guidance to evacuation routes are evaluated. Also assessed are the safe use of medical gases, the management (including storage, use, and leakage) of hazardous substances, electrical safety, environmental safety in rooms with heavy machinery, disaster response, and emergency management. The primary objective is to scrutinize the safety vulnerabilities of the facilities and to critically evaluate the general environmental conditions at the two medical institutions, providing management teams with insights to enhance the safety of the health-care environment.</p> <p> </p>

Exploring Safety Vulnerability in Prefabricated Construction and Mitigation Effects of Internet of Things

Exploring Safety Vulnerability in Prefabricated Construction and Mitigation Effects of Internet of Things

Numerical analysis of vapor dispersion from compressed hydrogen (H2) storage vessels

The rising demand for hydrogen fuel, driven by the emergence of fuel cell electric vehicles, underscores the need to optimize refueling station efficiency and affordability while prioritizing safety and performance. Compressed gas hydrogen storage emerges as a practical solution however safety across production, storage, and distribution is paramount for broader acceptance of hydrogen technologies. Any incidents could undermine public trust, emphasizing the importance of mitigating risks such as hydrogen leakage. This study investigates hydrogen dispersion and conducts consequence analyses for potential hazards, considering stability, ambient temperature, wind speed, and process parameters like vessel temperature, pressure, and leakage diameter. It assesses various scenarios, including high-pressure storage vessels and generic refueling station layouts, by employing integral models of ALOHA, PHAST and HyRAM. Findings showed that process parameters significantly influence hazard severity, with leakage diameter having a notable impact. Common safety vulnerabilities in fuel cell vehicles and refueling stations are highlighted, emphasizing adherence to international regulations and standards for enhanced safety protocols.

Numerical Investigation of the Thermal Effect of Material Variations on the Brake Disc

Braking is one of the most critical systems for ensuring the safe driving performance of motor vehicles. Among the components that constitute the braking system, the disc is the one with the highest risk of wear. When the braking system is engaged, the physical contact between the brake pad and the disc leads to the generation of high pressure and high temperature. This released heat shortens the material's lifespan. The wearing or fracturing of the brake disc can result in a significant safety vulnerability. Therefore, it is desired for the discs to have better heat dissipation to increase their longevity. In this study, two different designs of brake discs were first examined thermally using the computational fluid dynamics (CFD) method. Subsequently, numerical analyses were conducted under the same boundary conditions by selecting the disc geometry with superior heat dissipation performance and using different materials. The results obtained from the analyses were compared and interpreted. The materials utilized in the study were grey cast iron, carbon steel, stainless steel, and carbon-carbon composite. As a result, it was observed that the carbon-carbon composite exhibited higher resistance to elevated temperatures.

Long-Term Sustainability and Adaptation of I-PASS Handovers.

Inadequate communication during transitions of care is a major health care quality and safety vulnerability. In 2013 Massachusetts General Hospital (MGH) embarked on a comprehensive training program using a standardized handover system (I-PASS) that had been shown to reduce adverse events by 30% even when not completely executed on each patient. In this cross-sectional study, the authors sought to characterize handover practices six years later. Using a standardized interview tool, the researchers evaluated handovers between responding clinicians in 10 departments and then validated these findings through direct observations, allowing for flexibility and customization in the I-PASS elements. The study qualitatively compared I-PASS element use in verbal handovers to MGH early postintervention data, as well as verbal and written handovers with the I-PASS Study Group's postintervention results. The authors observed 156 verbal and reviewed 182 written patient handovers. Ninety percent of departments adhered at least partially to the I-PASS system. Average handover duration ranged from 0.6 to 2.1 minutes per established patient. The service with best I-PASS adherence also consistently included the most information per unit of time. Acknowledging substantial differences in study technique, MGH adherence was, on average, comparable or better on all I-PASS elements in verbal handovers and on three of four elements of written handovers compared with the I-PASS Study Group's postintervention results. Although uptake has varied across services, six years after hospitalwide implementation of I-PASS, the majority of services are performing structured and sequenced handovers, most of which include some elements of the I-PASS system. Those services with the best I-PASS adherence conducted the most efficient handovers.

Software Engineering and Ethics: Navigating the Ethical Landscape in the Digital Age

In the unexpectedly evolving landscape of software engineering, moral considerations have emerged as an important cornerstone, guiding the moral compass of professionals in the digital age. This summary encapsulates a comprehensive assessment exploring the difficult dating between software engineering and ethics. It navigates through the moral challenges faced by software engineers, inclusive of privateer’s issues, safety vulnerabilities, algorithmic biases, and intellectual property problems. Delving into established moral choice-making frameworks, inclusive of utilitarianism and deontology, this overview analyzes their application in actual global software engineering situations. Through insightful case studies, this paper sheds mild on historic and contemporary moral dilemmas, providing treasured classes for the software program engineering community. The assessment additionally emphasizes the significance of promoting ethical practices within businesses, advocating for transparency, responsibility, and a tradition of obligation. Looking in advance, the paper discusses the future of software engineering and ethics, exploring the moral dimensions of emerging technology like artificial intelligence and the Internet of Things. By supplying a roadmap for ethical software program development, this evaluation underscores the vital for software engineers to navigate the ethical complexities, fostering a moral virtual ecosystem for present-day and future generations.

Evaluation of forest fire risk based on multicriteria decision analysis techniques for Changzhou, China

With the rapid development of China's economy, an increasing number of cities are building forest parks or landscape forests to improve the quality of people's lives and protect the environment. However, forest fires are notoriously difficult to extinguish and have caused tragic losses to cities. Therefore, it is particularly important to conduct a comprehensive assessment of forest fire risk. This study focuses on urban forest fire risk to develop an integrated fire risk model to prevent forest fires. A risk assessment model covering different multicriteria decision analysis (MCDA) techniques was developed to analyze safety vulnerabilities and determine fire risk levels with the spatial analysis functions of geographical information system (GIS) software. Moreover, the accuracy of the model was verified using fire data downloaded from the NASA-FIRMS website. The area under the ROC curve (AUC) was used to evaluate the accuracy of each model. The results show that the AUC value of the combination weighting of game theory (CWGT) method is 0.700. The method has good accuracy and can be used to predict the forest fire risk in Changzhou City. The towns with the highest forest fire risk in Changzhou City were Tianmuhu, Daibu, and Xuebu, with average areas of 8354.77 ha, 5125.92 ha, and 5256.67 ha, respectively, and proportions of 36.20 %, 37.15 %, and 23.43 %, respectively. The conclusion of this study provides a reference and guide for local government management. At the same time, this study considers the emergency rescue capability index and comprehensively evaluates the risk over a time series of pre- and postfire. This is a new attempt and has made new contributions to this field.

Analisis Faktor-Faktor Kerentanan Banjir Pesisir (Studi pada Pesisir Pantai Utara Jawa Tengah)

Flooding is a natural phenomenon that occurs in Indonesia, including the coastal areas of Pantura) which are complicated problems for the government that have not been fully handled properly. Pantura coastal flooding occurs due to rainwater flooding and tidal flooding, causing vulnerability. This study aims to determine the factors that cause vulnerability and determine the adaptation strategy for Pantura coastal flooding. This type of research is quantitative research because research is in the form of case studies based on descriptive assessments and the existence of quantitative data based on numbers and research is carried out by observation and data collection using data collection techniques and data analysis with SPSS Version 25.00 with 183 respondents. The research location is the coastal area of Pantura. Primary data collection method with questionnaires, interviews, observations, and documentation,. Based on the results of the study, the factors of coastal flood vulnerability are classified into physical vulnerability of buildings and infrastructure, biophysical and hydrological vulnerability, vulnerability of community capability and safety and flood vulnerability. The validity test of the vulnerability of coastal flooding shows all valid and reliable indicators because it has a validity value of > 0.300 and the reliability test value of the cronbrach alpha research instrument is above 0.700.

CuCatch: A Debugging Tool for Efficiently Catching Memory Safety Violations in CUDA Applications

CUDA, OpenCL, and OpenACC are the primary means of writing general-purpose software for NVIDIA GPUs, all of which are subject to the same well-documented memory safety vulnerabilities currently plaguing software written in C and C++. One can argue that the GPU execution environment makes software development more error prone. Unlike C and C++, CUDA features multiple, distinct memory spaces to map to the GPU’s unique memory hierarchy, and a typical CUDA program has thousands of concurrently executing threads. Furthermore, the CUDA platform has fewer guardrails than CPU platforms that have been forced to incrementally adjust to a barrage of security attacks. Unfortunately, the peculiarities of the GPU make it difficult to directly port memory safety solutions from the CPU space. This paper presents cuCatch, a new memory safety error detection tool designed specifically for the CUDA programming model. cuCatch combines optimized compiler instrumentation with driver support to implement a novel algorithm for catching spatial and temporal memory safety errors with low performance overheads. Our experimental results on a wide set of GPU applications show that cuCatch incurs a 19% runtime slowdown on average, which is orders of magnitude faster than state-of-the-art debugging tools on GPUs. Moreover, our quantitative evaluation demonstrates cuCatch’s higher error detection coverage compared to prior memory safety tools. The combination of high error detection coverage and low runtime overheads makes cuCatch an ideal candidate for accelerating memory safety debugging for GPU applications.

A Robust and Effective Two-Factor Authentication (2FA) Protocol Based on ECC for Mobile Computing

The rapid development of mobile computing (e.g., mobile health, mobile payments, and smart homes) has brought great convenience to our lives. It is well-known that the security and privacy of user information from these applications and services is critical. Without the prevention provided by an authentication mechanism, safety vulnerabilities may accumulate, such as illegal intrusion access resulting in data leakage and fraudulent abuse. Luckily, the two-factor authentication (2FA) protocols can secure access and communication for mobile computing. As we understand it, existing 2FA authentication protocols weaken security in the pursuit of high efficiency. How efficiency can be achieved while preserving the protocol’s security remains a challenge. In this study, we designed a robust and effective 2FA protocol based on elliptic curve cryptography (ECC) for authentication of users and service providers. We proved the robustness (respectively, the effectiveness) of the presented protocol with the heuristic analysis and security verification provided by the ProVerif tool (respectively, with a performance comparison based on six schemes). Performance comparisons in terms of message rounds, communication, and computation overheads showed that our scheme was superior to the exiting schemes or comparable as a whole; i.e., only two rounds, 1376 bits, and 1.818 ms were required in our scheme, respectively. The evaluation results showed that the proposed 2FA protocol provides a better balance between security and availability compared to state-of-the-art protocols.

Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images

Advanced Patch Attacks (PAs) on object detection in natural images have pointed out the great safety vulnerability in methods based on deep neural networks. However, little attention has been paid to this topic in Optical Remote Sensing Images (O-RSIs). To this end, we focus on this research, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., PAs on object detection in O-RSIs, and propose a more Threatening PA without the scarification of the visual quality, dubbed TPA. Specifically, to address the problem of inconsistency between local and global landscapes in existing patch selection schemes, we propose leveraging the First-Order Difference (FOD) of the objective function before and after masking to select the sub-patches to be attacked. Further, considering the problem of gradient inundation when applying existing coordinate-based loss to PAs directly, we design an IoU-based objective function specific for PAs, dubbed Bounding box Drifting Loss (BDL), which pushes the detected bounding boxes far from the initial ones until there are no intersections between them. Finally, on two widely used benchmarks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., DIOR and DOTA, comprehensive evaluations of our TPA with four typical detectors (Faster R-CNN, FCOS, RetinaNet, and YOLO-v4) witness its remarkable effectiveness. To the best of our knowledge, this is the first attempt to study the PAs on object detection in O-RSIs, and we hope this work can get our readers interested in studying this topic.

An application for collecting and mining reports referring vulnerabilities and exposures in physical systems: A comparative study of selected clustering methods

This paper describes an application dedicated to collecting and mining reports of software safety vulnerabilities and exposures in physical systems. This work focuses on the clustering problem of such reports, which means grouping them through automated computing process. The clustering is carried out in two stages. In the first stage, potential similarities between the reports, together with the number of clusters are detected through automated text analysis. In the second stage, a hierarchical clustering is conducted to reduce the number of these clusters to provide potential number of appropriate clusters of the group of reports. The clustering of the second stage provides the user with greater flexibility in viewing individual reports. This paper focuses mainly on the first stage of described clustering method. Two selected clustering algorithms have been compared with the aim to show how to detect the most appropriate number of groups between scraped documents. The computational experiment results are presented and discussed in the experimental section of this work.

Analysis of Relative Importance of Children's Disaster Vulnerability from the Perspective of Social Welfare

Discussion and reference to children's vulnerability to disasters has only recently begun. Therefore, studies on children's vulnerability to disasters are still lacking. Therefore, the purpose of this study is to investigate the factors of children's vulnerability in the event of a disaster from the point of view of social welfare, and to analyze the relative importance of children's vulnerability to disasters for experts. Through this, it is hoped that children's vulnerability to disasters will be reflected in future disaster policy making. The analysis results are as follows. Looking at the opinions of experts, in terms of the vulnerability of children to disasters in the event of a disaster, psychological and safety vulnerabilities were recognized as the most important. Second, experts recognized that physical vulnerability was important. Finally, experts recognized developmental vulnerability as important. In particular, considering the complex weight, disaster welfare experts recognized the most important vulnerability among children's vulnerability in the event of a disaster, including dependence on their parents.

Robust and Explainable Semi-Supervised Deep Learning Model for Anomaly Detection in Aviation

Identifying safety anomalies and vulnerabilities in the aviation domain is a very expensive and time-consuming task. Currently, it is accomplished via manual forensic reviews by subject matter experts (SMEs). However, with the increase in the amount of data produced in airspace operations, relying on such manual reviews is impractical. Automated approaches, such as exceedance detection, have been deployed to flag safety events which surpass a pre-defined safety threshold. These approaches, however, completely rely on domain knowledge and outcome of the SMEs’ reviews and can only identify purely threshold crossings safety vulnerabilities. Unsupervised and supervised machine learning approaches have been developed in the past to automate the process of anomaly detection and vulnerability discovery in the aviation data, with availability of the labeled data being their differentiator. Purely unsupervised approaches can be prone to high false alarm rates, while a completely supervised approach might not reach optimal performance and generalize well when the size of labeled data is small. This is one of the fundamental challenges in the aviation domain, where the process of obtaining safety labels for the data requires significant time and effort from SMEs and cannot be crowd-sourced to citizen scientists. As a result, the size of properly labeled and reviewed data is often very small in aviation safety and supervised approaches fall short of the optimum performance with such data. In this paper, we develop a Robust and Explainable Semi-supervised deep learning model for Anomaly Detection (RESAD) in aviation data. This approach takes advantage of both majority unlabeled and minority labeled data sets. We develop a case study of multi-class anomaly detection in the approach to landing of commercial aircraft in order to benchmark RESAD’s performance to baseline methods. Furthermore, we develop an optimization scheme where the model is optimized to not only reach maximum accuracy, but also a desired interpretability and robustness to adversarial perturbations.

Safety and Security Issues, Gender-Based Violence and Militarization in the Time of Armed Conflict: The Experiences of Internally Displaced People From Marawi City

This study delves into the experiences of armed conflict and displacement among civilians, who evacuated from the Islamic City of Marawi to nearby cities and municipalities in Northern Mindanao, as well as other parts of the Philippines, to escape the clashes between ISIS-affiliated extremists and security forces in 2017. Drawing upon in-depth interviews and focus group discussions with survivors of armed conflict and duty-bearers, such as government employees, staff of non-government organizations (NGOs), doctors, faculty members and administrators of educational institutions, and volunteers who aided in relief efforts, this research identifies the safety and security issues and vulnerabilities confronting internally displaced people (IDPs) from Marawi City, who are predominantly racial, ethnic, and religious minorities. This study investigates the trends in and risks for gender-based violence among women and girls and men and boys in conflict zones and the challenges in the promotion of their safety and well-being. This paper examines the dynamics of gender-based violence and the respondents’ experiences of private, community-based, and state-sponsored violence in conflict zones and the risk of further violence upon their return to Marawi City. This research also examines the experiences of militarization among IDPs and their views of Martial Law, which was declared in Mindanao on the first day of the Marawi Siege in May 2017. This study illuminates the nuances in the experiences of IDPs living in traditional evacuation centers and alternative home-based evacuation arrangements, their service needs, and the support systems and interventions available to them. The researcher highlights the links between racial, ethnic, gender, and social class inequality in the Philippines and the vulnerability of IDPs, given their dismal living conditions and the absence of normalization in their lives due to the prolonged siege. This paper highlights the intersections between private and public violence, the human rights issues confronting IDPs from Marawi City, and the local and international responses to their situation.

Enhanced Fuzzing System for Proactive Security Gap Exposure

Lots of safety vulnerabilities have been harming software and hardware. Grey box fuzzer is one of the most successful methods for automatic vulnerability detection. Most research focus on using fuzzer for SW quality check only. This paper introduced how to leverage fuzzer to exposure security gap as early as possible. Conventional Grey box Fuzzers like AFL can open perform fuzzing against the whole input and spend more time on smaller seeds with lower execution time, which significantly impact fuzzing efficiency for complicated input types. In this work, we introduce one intelligent grey box fuzzing for Intel Media driver, MediaFuzzer, which can perform effective fuzzing based on selective fields of complicated input. Also, with one novel calling depth-based power schedule biased toward seed corpus which can lead to deeper calling chain, it dramatically improves the vulnerability exposures (~6.6 times more issuesexposed) and fuzzing efficiency (~2.7 times more efficient) against the baseline AFL for Intel media driver with almost negligible overhead.

Provenance of Groundwater Solute and Its Controlling Factors in Yancheng Area

Affected by human activities and climate change, the deterioration of groundwater quality could continue to intensify, and it is difficult to repair after being polluted. In order to reduce the vulnerability of groundwater safety, it is urgent to strengthen research on groundwater quality. To analyze the characteristics of hydrochemical composition and control factors and to explore the impact of human activities, groundwater sampling was conducted in the alluvial and marine plains of Yancheng area, where industrial and agricultural activities were intense. The results showed that:① the TDS value of shallow groundwater was between 211 and 3790 mg·L-1, of which, brackish water samples accounted for 30.28% in Yancheng. The order of the concentration of anions and cations was:HCO3->Cl->SO42->NO3- and Na+>Ca2+>Mg2+>K+. However, from Type Ⅰ/Ⅱ water to Type Ⅴ water, with the increase in TDS value, the dominant anions and cations evolved from HCO3 type to Cl/SO4 type and from Ca type to Na type, respectively. ② In the initial Type Ⅰ/Ⅱ water bodies, Na+ should have mainly originated from the weathering erosion of albite, whereas Ca2+, Mg2+, and HCO3- should have had a common rock weathering source. In the subsequent water quality evolution process, the factors that led the TDS value to increase, or even to exceed the standard, were not limited to rock weathering, and the effects of evaporation-concentration and ion exchange were also very worthy of attention. ③ The enrichment factor analysis showed that the Cl- and SO42- were mainly from the input of sea salt sources, and their proportions were gradually increasing from the freshwater areas of Type Ⅰ/Ⅱ in the west to the brackish water areas in the east. However, Ca2+, K+, and HCO3- were mainly derived from crustal sources, and Ca2+ was precipitated due to saturation. ④ The NO3- derived from agricultural fertilizers was limited to water of category Ⅰ/Ⅱ and Ⅲ-1 (500 mg·L-1 < TDS ≤ 750 mg·L-1). Discharge of urban sewage mainly affected Type Ⅲ water bodies. The nitrates formed by agricultural organic fertilizers were mainly distributed in the water of Type Ⅳ and Ⅴ, which was the main rice-producing area of Sheyang, Jiangsu, and the SO42- formed by human activities in this area could be ignored. These should be one of the achievements in the development of regional ecological agriculture.

Toward Advanced UAV Communications: Properties, Research Challenges, and Future Potential

In recent times, communication and connectivity requirements have been evolving rapidly, and it is evident with the recent deployment of fifth-generation (5G) cellular networks worldwide. Similarly, unmanned aerial vehicles (UAVs) have gained interest in several applications, including surveillance, monitoring, geographic mapping, and logistics, among others. Therein, 5G cellular network support for UAVs is gaining both attention and momentum within various regulatory bodies and consortiums. While 5G is gaining momentum to establish necessary communication technologies for UAV operations, we provide a state-of-the-art overview emphasizing the technical challenges of applying millimeter-wave technology to support UAV communication. This article provides a closer look at the key enabling technologies and their potential issues impacting UAV operation under a unifying 5G network and beyond. In addition, this article also discusses the security issues impacting network privacy and safety vulnerabilities of UAVs. To conclude, we identify several open research issues that drive UAV deployment and operations and demand further study.