Selection Of Countermeasures Research Articles

Methods of countering attacks and organizing the choice of channel protection tools when controlling aviation robotic devices

The purpose of the research is identification of organizational and technical methods of countering attacks on command and telemetry data transmission lines between aviation robotic devices and ground control points, as well as the formation of comprehensive information protection of the communication channels under consideration.Methods. The scientific article substantiates methods for mitigating and countering attacks at the physical level of the ISO/OSI open systems interaction model for organizing communication using the MAVLINK protocol for the purpose of controlling aviation robotic devices (noise-resistant coding with a high code rate (using the example of the most common code - Viterbi), extension method Frequency Hopping Spectrum (FHSS), Direct Sequence Spread Spectrum (DSSS), Multiple Input Multiple Output (MIMO) technology). A method for organizing the selection of protection and countermeasures when controlling aviation robotic devices is outlined, which consists of assessing the risks and consequences of the implementation of vulnerabilities in order to introduce countermeasures there and in such quantities that they have the greatest benefit.Results. The article discusses the main possibilities for intentional interference on the control and data transmission channels of consumer aviation robotic devices, an algorithm for assessing the risk of "leaving the route" is considered; a risk assessment option is provided in relation to the risk of an aircraft going off route, indicating the name of the attack, its likelihood, impact and resulting risk, as well as the acceptability of this risk and recommendations regarding the urgency of mitigating it; recommendations for countermeasures are presented in accordance with the acceptability of likelihood and impact; describes the relationship between risk and countermeasures used to reduce unauthorized exposure, in accordance with weighting coefficients.Conclusion. The scientific article discusses the main methods of countering attacks and unauthorized access when controlling autonomous robotic devices. The risk coefficients of attacks and countermeasures taken are considered.

The impact of indirect benefits (reduced travel time, fuel use and emissions) in cost benefit analysis of road safety countermeasures.

In cost benefit analysis of road safety countermeasures, all relevant effects on safety, travel time and environment have a substantial impact during economic appraisal. However, in the most widely used road safety appraisal tools such as SafetyAnalyst and International Road Assessment Programme (iRAP), indirect effects related to travel time and environment are not considered. Most economic appraisal studies conducted for road safety countermeasures consider only the safety benefits and ignore the indirect benefits due to lack of models to evaluate them. This study attempts to document the quantitative impact of indirect benefits during economic appraisal of road safety infrastructure investments particularly from the angle of reduced crashes. To this effect, data from 9 European countries and the 20-year infrastructure improvement programme developed for the Netherlands are applied to demonstrate the impact of these indirect benefits through a quantitative study. The results show that indirect benefits increase the value of benefits by 7%, which improves the cost effectiveness of countermeasures. Consequently, the number of countermeasures selected for implementation are increased due to addition of these benefits. Travel time benefits constitute the largest share of indirect benefits with a contribution of 6% to the overall benefits due to countermeasure implementation. In conclusion, indirect benefits have a substantial impact on the computation of benefits and countermeasure selection process. In order to present improved business cases for road safety infrastructure investments, there is need to include these benefits during economic appraisal process. Travel time benefits have the highest portion of all indirect benefits compared to vehicle operating costs (VOCs) and emission benefits. The study recommends conducting more research related to travel time benefits due to countermeasure implementation.

Characteristic of Micro-Mobility Devices’ Users in Malaysia

Introducing micro-mobility devices (MMD) such as bicycles and e-scooters as a transport option in Malaysia and the risk safety issues have attracted the attention of the government and enforcement agencies especially when they are cycling or riding on the road. Its uncontrolled use has made the government decision to ban the use of certain MMDs on the road with effect from the beginning of 2022. However, in order to support smart mobility, green and active mobility, the government is devising a mechanism to enable the use of MMDs in some areas. Therefore, this paper explores the characteristics and demographics of existing MMD users in Malaysia in order to assist the government give careful consideration to the safety of MMD users. The government must decide on the best ecosystem to implement the use of MMD through a safe control mechanism in terms of vehicles, infrastructure and users. To achieve an objective to assist in the decision-making process for the selection of appropriate countermeasures to promote the safety of MMD users, this study aims to assess and understand the characteristics of MMD users in Malaysia. This study only focused on existing MMD users. Throughout the online survey, a total of 371 respondents were involved in this study of which 277 were MMD users. This study also revealed that most MMD users in Malaysia are male, young (teenagers and adults) and have a fixed income.

Injury severity of single-vehicle weather-related crashes on two-lane highways

Roadway safety is immensely affected during adverse weather conditions. Therefore, this study seeks to quantify such effects on the injury severity of involved drivers in single-vehicle crashes on two-way highways. This should be done by identifying risk factors and accommodating the heterogeneous impacts of these factors by accounting for the heterogeneity in means and variances of random parameters in the estimated model using seven years of police-reported crash data from 2010 to 2016 in Oregon. The study findings confirm the superior performance of the mixed logit model with heterogeneity in the means and variances in terms of statistical fit comparable to the traditional mixed logit model and mixed logit model with heterogeneity in the means only. The estimation results reveal that some factors such as female drivers, fatigued drivers, crashes in the early morning (between 4:00 am and 8:00 am), and crashes that occurred in darkness with no streetlights were found to increase serious injury outcomes. The empirical findings can offer evidence-based insights to help in decision-making and countermeasure selection that is aimed at improving safety under adverse weather conditions.

A multilevel decision-making approach for road infrastructure management

ABSTRACT The design of effective road safety countermeasures requires a network diagnosis supported by data. Moreover, infrastructures need to be ready for the introduction of vehicle-to-infrastructure communications to support technologies, as truck platooning. Ascendi, a motorway concessionaire, developed an action plan to decrease crash frequency and casualties that rests in data recorded by automatic vehicle counting devices. To have a good network representation, new equipment will provide more data, thus enhancing the selection of countermeasures. We developed a multilevel decision-support approach to define equipment location. The process stages correspond to three levels of analysis: (1) clustering for road segment classification (network level); (2) quantification of the devices to install ensuring similar coverage (concession level); (3) device allocation according to geographical and cost criteria (segment level). An iterative and participatory process involving Ascendi resulted in a proposal for adding 43 devices to the existing 72, increasing the network coverage to 39%.

Using motor vehicle crash records for injury surveillance and research in agriculture and forestry

Problem: Fatal injuries in the agriculture, forestry, and fishing sector (AgFF) outweigh those across all sectors in the United States. Transportation-related injuries are among the top contributors to these fatal events. However, traditional occupational injury surveillance systems may not completely capture crashes involving farm vehicles and logging trucks, specifically nonfatal events. Methods: The study aimed to develop an integrated database of AgFF-related motor-vehicle crashes for the southwest (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) and to use these data to conduct surveillance and research. Lessons learned during the pursuit of these aims were cataloged. Activities centered around the conduct of traditional statistical and geospatial analyses of structured data fields and natural language processing of free-text crash narratives. Results: The structured crash data in each state include fields that allowed farm vehicles or equipment and logging trucks to be identified. The variable definitions and coding were not consistent across states but could be harmonized. All states recorded data fields pertaining to person, vehicle, and crash/environmental factors. Structured data supported the construction of crash severity models and geospatial analyses. Law enforcement provided additional details on crash causation in free-text narratives. Crash narratives contained sufficient text to support viable machine learning models for farm vehicle or equipment crashes, but not for logging truck narratives. Discussion: Crash records can help to fill research and surveillance gaps in AgFF in the southwest region. This supports traffic safety’s evolution to the current Safe System paradigm. There is a conceptual linkage between the Safe System and Total Worker Health approaches, providing a bridge between traffic safety and occupational health. Practical Applications: Despite limitations, crash records can be an important component of injury surveillance for events involving AgFF vehicles. They also can be used to inform the selection and evaluation of traffic countermeasures and behavioral interventions.

An Android Application-based Surface Oust Classifier

Surface dust can be a source carrier of viruses, bacteria and air pollution which entail common health issues such as asthma attacks, chest tightness, wheezing and difficulty in breathing. Visually perceived, cleanliness is one measure of indoor air quality and is the subjective assessment of cleaning quality. The aim of this work is to use pattern recognition mediated through a mobile application to analyse and classify dust in households, in order to obtain useful information about the dust sources for the selection of appropriate countermeasures in view to improve air quality and better manage the cleaning of indoor surfaces. The dust type categorization in this work are pollen, rock and ash. This paper also explores the concept of transfer learning techniques and adopts it for small particle classification using CNN models by developing a surface dust application for android smartphones. The behaviour of InceptionV2, InceptionV3, ResNetV2, MobileNetV2 and MobileNetV3 as dust feature extractors were analysed based on their accuracy, precision, recall and Fl-Scoreperformance metrics. Results show that MobileNetV3 model is best suited as a dust feature extractor and rapid dust prediction with an accuracy of up to 92% and low-size storage of only 30 megabytes. Additional Keywords: Android; Classification; Transfer Learning; Smartphone; Surface dust.

An Android Application-based Surface Dust Classifier

Surface dust can be a source carrier of viruses, bacteria and air pollution which entail common health issues such as asthma attacks, chest tightness, wheezing and difficulty in breathing. Visually perceived, cleanliness is one measure of indoor air quality and is the subjective assessment of cleaning quality. The aim of this work is to use pattern recognition mediated through a mobile application to analyse and classify dust in households, in order to obtain useful information about the dust sources for the selection of appropriate countermeasures in view to improve air quality and better manage the cleaning of indoor surfaces. The dust type categorization in this work are pollen, rock and ash. This paper also explores the concept of transfer learning techniques and adopts it for small particle classification using CNN models by developing a surface dust application for android smartphones. The behaviour of InceptionV2, InceptionV3, ResNetV2, MobileNetV2 and MobileNetV3 as dust feature extractors were analysed based on their accuracy, precision, recall and F1-Score performance metrics. Results show that MobileNetV3 model is best suited as a dust feature extractor and rapid dust prediction with an accuracy of up to 92% and low-size storage of only 30 megabytes.

Methods used for monitoring geodynamic processes on the territory of Bulgaria

The territory of Bulgaria is affected by almost all dangerous geodynamic processes, such as landslides, rockfalls, erosion, debris flows, abrasion, etc. In addition, the selection of countermeasures raises the serious issue of their periodic monitoring in order to protect human life and infrastructure. The general idea of this study is to provide information about the different methods used for monitoring the above-mentioned hazardous geodynamic processes. After a comprehensive review of the available scientific developments, the author found that in the field of engineering geology, articles related to the monitoring of given processes are almost absent. The purpose of the article is to provide detailed information about the various methods of monitoring dangerous geodynamic processes and their fields of application. Due to the limitation of the article`s volume, only the main features of the utilized and available monitoring methods in Bulgaria will be presented. Hence a further, more comprehensive overview will be in a great need.

Using Video Analytics to Improve Traffic Intersection Safety and Performance

Road safety has always been a crucial priority for municipalities, as vehicle accidents claim lives every day. Recent rapid improvements in video collection and processing technologies enable traffic researchers to identify and alleviate potentially dangerous situations. This paper illustrates cutting-edge methods by which conflict hotspots can be detected in various situations and conditions. Both pedestrian–vehicle and vehicle–vehicle conflict hotspots can be discovered, and we present an original technique for including more information in the graphs with shapes. Conflict hotspot detection, volume hotspot detection, and intersection-service evaluation allow us to understand the safety and performance issues and test countermeasures comprehensively. The selection of appropriate countermeasures is demonstrated by extensive analysis and discussion of two intersections in Gainesville, Florida, USA. Just as important is the evaluation of the efficacy of countermeasures. This paper advocates for selection from a menu of countermeasures at the municipal level, with safety as the top priority. Performance is also considered, and we present a novel concept of a performance–safety trade-off at intersections.

Intelligence in security countermeasures selection

Identifying security risks in organizations and also determining their severity in order to select appropriate security countermeasures is of great importance in organizations. In the last two decades, a lot of work has been done to increase the accuracy of risk impact calculation as well as the right selection of countermeasures. Also, a variety of work has been proposed to select combined countermeasures instead of single ones. So there is a challenge to balance the cost of security with the improvement of the defense system. In this paper, a dataset that includes the organization business processes, security data, assets, vulnerabilities, and related security countermeasures is suggested for the first time. In the previous work, this chain of information from the content of the organization, which is definitely different from another organization, has not been considered for the analysis of the performance of countermeasures (success or failure). Based on the results of the countermeasures during the organization’s lifetime, more efficient countermeasures can be suggested for new or existing risks. Therefore, by intelligently selecting the security countermeasures presented in this paper, organizations will be able to identify ineffective countermeasures and prevent them from being re-selected to counter attackers. In this way, we can make our organization more resilient to attackers over time.

Exposure Route Influences Disease Severity in the COVID-19 Cynomolgus Macaque Model.

The emergence of SARS-CoV-2 and the subsequent pandemic has highlighted the need for animal models that faithfully replicate the salient features of COVID-19 disease in humans. These models are necessary for the rapid selection, testing, and evaluation of potential medical countermeasures. Here, we performed a direct comparison of two distinct routes of SARS-CoV-2 exposure—combined intratracheal/intranasal and small particle aerosol—in two nonhuman primate species, rhesus and cynomolgus macaques. While all four experimental groups displayed very few outward clinical signs, evidence of mild to moderate respiratory disease was present on radiographs and at necropsy. Cynomolgus macaques exposed via the aerosol route also developed the most consistent fever responses and had the most severe respiratory disease and pathology. This study demonstrates that while all four models produced suitable representations of mild COVID-like illness, aerosol exposure of cynomolgus macaques to SARS-CoV-2 produced the most severe disease, which may provide additional clinical endpoints for evaluating therapeutics and vaccines.

Security Countermeasures Selection Using the Meta Attack Language and Probabilistic Attack Graphs

Connecting critical infrastructure assets to the network is absolutely essential for modern industries. In contrast to the apparent advantages, network connectivity exposes other infrastructure vulnerabilities that can be exploited by attackers. To protect the infrastructure, precise countermeasure identification is necessary. In this regard, the objective for the security officers is to identify the optimal set of countermeasures under a variety of budgetary restrictions. Our approach is based on the Meta Attack Language framework, which allows for convenient modelling of said infrastructures, as well as for automatic generation of attack graphs describing attacks against them. We formalize the problem of the selection of countermeasures in this context. The formalization makes it possible to deal with an arbitrary number of budgets, expressing available resources of both monetary and time-like nature, and to model numerous dependencies between countermeasures, including order dependencies, mutual exclusivity, and interdependent implementation costs. We propose a flexible and scalable algorithm for the problem. The whole methodology is validated in practice on realistic models.

Selection of countermeasure portfolio for shipping safety with consideration of investment risk aversion

This paper proposes optimization models to select shipping safety countermeasure portfolios with consideration of the investment risk preferences of decision-makers. The formulation is extended from the basic economic safety model, where losses are scenario-specific and are expressed as a function of the countermeasure selection variables based on analysing the chain of events along accident paths. Conditional value-at-risk is introduced to measure the investment risk represented by the worst-case cost. Several single -objective programming models are formulated first to optimize the safety-related costs using either the expectation criterion in risk-neutral environments or the conditional value-at-risk criterion in risk-averse environments. Then a bi-objective model is presented by combining those two criteria. Simulation results indicate that the best countermeasures for the expectation objectives are different from those for the conditional value-at-risk objectives. Such findings provide shipping safety investment planners insights in balancing not only the active investment and the accident loss but the return and the risk of the investment, to achieve a selection of countermeasures that is cost-effective as well as leads to low costs in extreme situations.

Towards improving sustainability of rail transport by reducing traffic delays at level crossings: A case study for the State of Florida

Rail transport makes a significant contribution to the economy of many geographical locations around the globe. Along with numerous merits, rail transport has some demerits as well, such as traffic delays and safety issues at level crossings. Traffic delays at level crossings have various negative impacts, including economic losses, increasing number of violations because of delays, queue spillbacks at the adjacent intersections, and environmental impacts. This study reviews a number of level crossing delay estimation approaches found in the literature. Based on the state-of-the-art review and practical considerations, a new approach for estimation of the overall traffic delay per day at level crossings with the existing warning devices is proposed to improve the sustainability of rail transport. Moreover, in order to assist with the selection of countermeasures for level crossing upgrades, the proposed approach can be also used to estimate the overall traffic delay per day at level crossings after implementation of countermeasures. A case study is conducted for the public and private level crossings in the State of Florida, considering the countermeasures commonly used in the United States. It was revealed that traffic delays were higher at the public level crossings than at the private level crossings, both before and after implementation of the candidate countermeasures. The case study also indicated that low-cost countermeasures could be more advantageous in terms of safety effectiveness and traffic delays as compared to many high-cost conventional countermeasures. Furthermore, certain attributes of the level crossings in Florida with the highest traffic delays were investigated.

Development and Evaluation of Geostatistical Methods for Estimating Weather Related Collisions: A Large-Scale Case Study

Winter driving conditions pose a real hazard to road users with increased chance of collisions during inclement weather events. As such, road authorities strive to service the hazardous roads or collision hot spots by increasing road safety, mobility, and accessibility. One measure of a hot spot would be winter collision statistics. Using the ratio of winter collisions (WC) to all collisions, roads that show a high ratio of WC should be given a high priority for further diagnosis and countermeasure selection. This study presents a unique methodological framework that is built on one of the least explored yet most powerful geostatistical techniques, namely, regression kriging (RK). Unlike other variants of kriging, RK uses auxiliary variables to gain a deeper understanding of contributing factors while also utilizing the spatial autocorrelation structure for predicting WC ratios. The applicability and validity of RK for a large-scale hot spot analysis is evaluated using the northeast quarter of the State of Iowa, spanning five winter seasons from 2013/14 to 2017/18. The findings of the case study assessed via three different statistical measures (mean squared error, root mean square error, and root mean squared standardized error) suggest that RK is very effective for modeling WC ratios, thereby further supporting its robustness and feasibility for a statewide implementation.

Selection of countermeasures against propagation of harmful information via Internet

Today, Internet allows accessing and propagation various information. Some of this information can be undesired for particular user or even harmful. While there are quite a large number of systems to respond to unwanted and harmful information and some well-established set of countermeasures, such as filtering, blocking access, and notification, existing systems are often based on one type of countermeasures. From our point of view, there is a need for the technique of automated selection of optimal countermeasures to counteract undesired or harmful information on the Internet depending on the type of such information and characteristics and needs of the protected system. In this paper we propose the set of interconnected models, including threat model, countermeasure model and information object model, and countermeasure selection technique for protection against harmful information in Internet using these models. The proposed technique uses single-criteria optimization on the basis of introduced countermeasure selection index and allows selecting applicable and optimal countermeasures for the particular system from specific threats. The application of the technique is demonstrated on the experiments.

A Bio-Inspired Reaction Against Cyberattacks: AIS-Powered Optimal Countermeasures Selection

Nowadays, Information and Communication Technology (ICT) infrastructures play a crucial role for human beings, providing essential services at astonishing speed. Nevertheless, such a centrality of those infrastructures attracts the interest of ill-motivated actors that target such infrastructures with cyberattacks that are every day more sophisticated and more disruptive. In this alarming context, selecting the optimal set of countermeasures represents a primary need to react against the appearance of potentially dangerous threats effectively. With the motivation to contribute to develop ing faster and more effective response capabilities against them, the paper at hand introduces a novel cybersecurity reaction methodology based on Artificial Immune Systems (AIS), for which the evolutionary computing paradigm has been adopted. By leveraging the outstanding properties of these bio-inspired techniques, the selected countermeasures to defeat cyberthreats through cloning and mutation phases in an effort to improve the quality of the solution from a quantitative perspective, being able to adjust the risk to which the assets of the protected system are exposed. Exhaustive experiments demonstrate the feasibility of the proposed approach, reducing the risk in a more than acceptable time lapse.

Stateful RORI-based countermeasure selection using hypergraphs

Cost-sensitive metrics have been widely used during the past years as financial metrics that quantify the monetary costs and benefits of security investments, assess risks, and select countermeasures accordingly. However, due to the complexity of current attacks, and the level of dynamicity required in the estimation of the parameters composing the metrics, the use of a novel approach that considers restrictions, inter-dependency, as well as, the previous and current state at which the system is exposed to, is required. We propose in this article a Stateful Return on Response Investment (denoted by StRORI) that uses hypergraphs to model actions that have been previously deployed (e.g., at state ST0) while the current state of the system (e.g., ST1) is under analysis. As a result, StRORI is a dynamic tool that considers the changes of the system in terms of number of active devices, previously deployed countermeasures, the cost of adding a new countermeasure or suppressing a previously deployed one, and the effectiveness of a group of security measures due to the implementation of a given action. A case study is presented about the integration of the StRORI index with hypergraph models to assess countermeasures against cyber attacks.

Stateful RORI-based countermeasure selection using hypergraphs

Cost-sensitive metrics have been widely used during the past years as financial metrics that quantify the monetary costs and benefits of security investments, assess risks, and select countermeasures accordingly. However, due to the complexity of current attacks, and the level of dynamicity required in the estimation of the parameters composing the metrics, the use of a novel approach that considers restrictions, inter-dependency, as well as, the previous and current state at which the system is exposed to, is required. We propose in this article a Stateful Return on Response Investment (denoted by StRORI) that uses hypergraphs to model actions that have been previously deployed (e.g., at state ST0) while the current state of the system (e.g., ST1) is under analysis. As a result, StRORI is a dynamic tool that considers the changes of the system in terms of number of active devices, previously deployed countermeasures, the cost of adding a new countermeasure or suppressing a previously deployed one, and the effectiveness of a group of security measures due to the implementation of a given action. A case study is presented about the integration of the StRORI index with hypergraph models to assess countermeasures against cyber attacks.