Infrastructure Threats Research Articles

Resilient cloud cluster with DevSecOps security model, automates a data analysis, vulnerability search and risk calculation

Automated, secure software development is an important digitalization task, solved with the DevSecOps approach. An important part of the DevSecOps approach is continuous risk assessment, which is necessary to identify and evaluate risk factors. Combining the development cycle with continuous risk assessment creates software development and operation synergies and minimizes vulnerabilities. The article presents the main methods of deploying web applications and ways to increase information security at all stages of product development. It also compares different types of infrastructures and cloud computing providers and analyzes modern tools used to automate processes. The cloud cluster was deployed using Terraform and the Jenkins pipeline, written in the Groovy programming language, which checks program code for vulnerabilities and allows you to fix violations at the earliest stages of developing secure web applications. The developed cluster implements the proposed algorithm for automated risk assessment based on the calculation (modeling) of cloud infrastructure threats and vulnerabilities, which operates in real-time, periodically collecting all information and adjusting the system according to the risk and applied controls. The algorithm for calculating risk and losses is based on statistical data and FAIR information risk assessment methodology. The risk value obtained using the proposed method is quantitative, which allows more efficient forecasting of information security costs in software development.

Mapping release and propagation areas of permafrost-related rock slope failures in the French Alps: A new methodological approach at regional scale

Rock slope failure (RSF) from permafrost-affected rockwall is an emerging threat for human lives and infrastructure which raises a need for RSF hazard assessments in the alpine environment. This study proposes the first mapping approach to RSF release and propagation areas in the French Alps.By analysing a unique RSF database (1389 recent events in the Mont Blanc massif), we determine three levels of RSF Susceptibility Indexes (RSI) to map the potential release areas. We highlight a strong correlation between RSF and permafrost: 99 % of RSFs occurred from rockwalls with a Mean Annual Rock Surface Temperature (MARST) ≤ 3 °C and 35 % with MARST from −2 to 0 °C.The release area maps (34 km2 to 284 km2 depending on the RSI) are entered into a simple propagation model (RockavELA), using a dimensionless area-based energy line principle (i.e. reach susceptibility) to map propagation areas of potential RSFs. Three propagation limits are proposed, fitting the propagation characteristics of another RSF database containing 3497 events observed across the European Alps with heterogeneous propagation substrates, and merged with 48 additional events from high mountains in the French Alps. RockavELA reproduced the runout extent of 20 high mountain RSFs with <50 % of frontal and lateral error. Output maps show little sensitivity to the propagation limits (<25 % difference between the high and low propagation limits).This work is a preliminary step to identify potential hazard hot spots in the French Alps, and proposes a novel approach that could be applied in other mountain ranges.

Proglacial slopes are protected against erosion by trait diverse and dense plant communities associated with specific microbial communities

Soil erosion is a severe threat for ecosystems and anthropogenic infrastructure. Glaciers are retreating rapidly due to global warming and the receding ice leaves unvegetated depositions of sediment, which are prone to mobilisation during precipitation events. Vegetation is known to serve as erosion protection, but how above- and belowground plant traits, vegetation cover and plant diversity on community level are jointly affecting erosion is unclear. Additionally, soil microbial communities may have effects on slope stability by promoting plant growth and function. We measured sediment transport on 30 plots of 2 × 3 m size on proglacial slopes of the Gepatschferner glacier (Kaunertal, Austria) over three years in a natural pristine high alpine environment. Vegetation cover, species abundances and relevant above- and belowground traits were measured for each occurring plant species on community level. Path model analysis revealed that dense and species-rich plant cover characterized by specific plant growth strategies and trait diversity was best suited to decrease erosion. Vegetation properties were also closely linked to the composition of soil microbial communities characterised by next generation amplicon sequencing, which may facilitate soil formation and further enhance the plants’ soil stabilising potential. Our study illustrates that erosion control may benefit from a high vegetation cover, which is facilitated by diverse plant communities with complementary morphological traits. These vegetation properties also affect soil microbiota, but their impact on slope protection in combination with the vegetation remains to be elucidated. Our findings may inform nature-based solutions against erosion such as revegetation of alpine slopes using specific seed mixtures.

Predicting Cybersecurity Threats in Critical Infrastructure for Industry 4.0: A Proactive Approach Based on Attacker Motivations.

In Industry 4.0, manufacturing and critical systems require high levels of flexibility and resilience for dynamic outcomes. Industrial Control Systems (ICS), specifically Supervisory Control and Data Acquisition (SCADA) systems, are commonly used for operation and control of Critical Infrastructure (CI). However, due to the lack of security controls, standards, and proactive security measures in the design of these systems, they have security risks and vulnerabilities. Therefore, efficient and effective security solutions are needed to secure the conjunction between CI and I4.0 applications. This paper predicts potential cyberattacks and threats against CI systems by considering attacker motivations and using machine learning models. The approach presents a novel cybersecurity prediction technique that forecasts potential attack methods, depending on specific CI and attacker motivations. The proposed model's accuracy in terms of False Positive Rate (FPR) reached 66% with the trained and test datasets. This proactive approach predicts potential attack methods based on specific CI and attacker motivations, and doubling the trained data sets will improve the accuracy of the proposed model in the future.

Recent gully erosion intensity in an agricultural landscape underlain by fluvioglacial sediments (NE Czechia)

AbstractInappropriate agricultural management practices combined with intense rainfall often lead to gully erosion. Knowledge of the causes and consequences of past erosion pulses is essential not only for farmers to improve land management, but also for municipalities to define infrastructure threats associated with material transport. This study comprehensively evaluates accelerated gully erosion and estimates gully transport capacity in fluvioglacial sediments left by a Pleistocene ice sheet in northeastern Czechia. Geomorphic mapping of an area of approximately 2 km2 revealed gullies up to 3.5 m wide and deep, with their heads starting at the boundaries of fields and pastures. The ground‐penetrating radar survey confirmed the presence of soil pipes contributing to gully formation due to unmaintained field drainage combined with the natural occurrence of soil pipes. Microscopic analysis and dendrogeomorphic dating of 102 cross‐sections from 28 exposed tree roots revealed 18 years of gully incision since 1985, with increased activity from 2007 to 2014. The mean incision rates ranged between 1–20 cm/year, but could reach 1 m during individual events. More intensive incisions were typical for soils with higher smectite content. The last severe erosion event in 2014 caused clogging of culverts and damage to infrastructure by transporting boulders up to 34 cm in diameter at critical flow velocities of 2.4–2.8 ms−1. Considering the predominance of human‐induced factors (e.g., current soil compaction due to grazing and failure to maintain the drainage pipe outlets), ongoing gully erosion can be expected during intense rainfall events unless appropriate agricultural or stabilization measures are addressed.

Small Fixed-Wing UAV Radar Cross-Section Signature Investigation and Detection and Classification of Distance Estimation Using Realistic Parameters of a Commercial Anti-Drone System

Various types of small drones constitute a modern threat for infrastructure and hardware, as well as for humans; thus, special-purpose radar has been developed in the last years in order to identify such drones. When studying the radar signatures, we observed that the majority of the scientific studies refer to multirotor aerial vehicles; there is a significant gap regarding small, fixed-wing Unmanned Aerial Vehicles (UAVs). Driven by the security principle, we conducted a series of Radar Cross Section (RCS) simulations on the Euclid fixed-wing UAV, which has a wingspan of 2 m and is being developed by our University. The purpose of this study is to partially fill the gap that exists regarding the RCS signatures and identification distances of fixed-wing UAVs of the same wingspan as the Euclid. The software used for the simulations was POFACETS (v.4.1). Two different scenarios were carried out. In scenario A, the RCS of the Euclid fixed-wing UAV, with a 2 m wingspan, was analytically studied. Robin radar systems’ Elvira Anti Drone System is the simulated radar, operating at 8.7 to 9.65 GHz; θ angle is set at 85° for this scenario. Scenario B studies the Euclid RCS within the broader 3 to 16 Ghz spectrum at the same θ = 85° angle. The results indicated that the Euclid UAV presents a mean RCS value (σ ¯) of −17.62 dBsm for scenario A, and a mean RCS value (σ ¯) of −22.77 dBsm for scenario B. These values are much smaller than the values of a typical commercial quadcopter, such as DJI Inspire 1, which presents −9.75 dBsm and −13.92 dBsm for the same exact scenarios, respectively. As calculated in the study, the Euclid UAV can penetrate up to a distance of 1784 m close to the Elvira Anti Drone System, while the DJI Inspire 1 will be detected at 2768 m. This finding is of great importance, as the obviously larger fixed-wing Euclid UAV will be detected about one kilometer closer to the anti-drone system.

A Systematic Review of Smart City Infrastructure Threat Modelling Methodologies: A Bayesian Focused Review

Smart city infrastructure and the related theme of critical national infrastructure have attracted growing interest in recent years in academic literature, notably how cyber-security can be effectively applied within the environment, which involves using cyber-physical systems. These operate cross-domain and have massively improved functionality and complexity, especially in threat modelling cyber-security analysis—the disparity between current cyber-security proficiency and the requirements for an effective cyber-security systems implementation. Analysing risk across the entire analysed system can be associated with many different cyber security methods for overall cyber risk analysis or identifying vulnerability for individually modelled objects. One method for performing risk analysis proposed in the literature is by applying Bayesian-based threat modelling methodologies. This paper performs a systematic literature review of Bayesian networks and unique alternative methodologies for smart city infrastructure analysis and related critical national infrastructures. A comparative analysis of the different methodological approaches, considering the many intricacies, metrics, and methods behind them, with suggestions made for future research in the field of cyber-physical threat modelling for smart city infrastructure.

Anomaly detection in smart agriculture systems

This work proposes a new approach for Anomaly Detection in smart agriculture systems. Through the use of multi-sensor systems and Decision Support Systems, it is possible to collect, analyze, and process huge amounts of data on agriculture. This supports the farmer in the decision-making process to optimize the results in terms of quality and quantity, to avoid waste, and to maximize profits. However, the use of IoT and intelligent communication technologies can introduce a number of intentional and unintentional weaknesses and flaws in data and information management. The research proposes an Anomaly Detection System (a multi-layered architecture) to mitigate the infrastructure threats in a pro-active way in the Smart Agriculture domain. The design of the proposed architecture is based on a machine-learning algorithmic approach by a multivariate linear regression (MLR) and a long-term memory neural network algorithm (LSTM). The application of the Anomaly Detection System was done on a real dataset coming from a smart agriculture system located in the Apulia region (Italy).

Judicial Independence in Pakistan

Independent judiciary is the justification of a balanced and disinterested and judiciously equitable civilization. Independence suggests that professionals in law can unobstructed deliver legal judgments whether involving powerful diplomats, executive characters, or average residents. Therefore, guaranteeing resolutions are built on vitality slightly than is the outcome of governmental tensions or is promoting some bulk. Founded with freedom, the judiciary system works as a defense of public equities and independence. In Pakistan, however, the Constitution designates an individualistic and non-interrupted judiciary but political authorities, from multiple years, have been doubled upon guaranteeing that the lawmakers or law implementers/ judges constantly survive in an environment of anxiety and address partial and advantageous determinations under the leadership and intentional power of the outside officials. This paper settles that an unconventional or independent judicial system may not denote the clarification to all the dilemmas of Pakistan but is the numerous significant paces towards resolving the obstacles covered by the country machinations. These hurdles have great impact on country’s overall judicial performance which turns it not a democratic state and throw critical observation over dependent proclaims of judicial system, where jury could not perform and execute rule of law individually. Furthermore, the historical events from 1947 to 1956 stand the revolution in constitutional corner of the state but unfortunately severe contamination in legislative system and interpretation of external bodies in organizational independency of law, upgraded nepotism and corruptions like threats in current infrastructure of administration.

Guest Editorial: Privacy-Preserving Federated Machine Learning Solutions for Enhanced Security of Critical Energy Infrastructures

Security is paramount in protecting critical infrastructures such as healthcare, power, water distribution, and transport system. Modern critical infrastructures are increasingly turning into distributed, complex Cyber-Physical systems that enable data monitoring in near real-time conditions. With the advancement in distributed IoT systems, the magnitude of the threats has increased. An innovative approach is to adopt Privacy-Preserving machine learning solutions to detect any possible anomalies in such infrastructures. This issue aims to contribute to the body of knowledge on enhancing critical infrastructure security in developing machine learning solutions and benefiting our future fighting against cyber threats in critical infrastructure.

Non-nuclear Electromagnetic Pulse Threat of Critical Infrastructures and Protection Strategies

Major infrastructures are vital for the modernization and operation of a country and the safety of major infrastructures is crucial for national security. In recent years, the generation technology of non-nuclear electromagnetic pulse (EMP) sources has gradually matured and develops to be universal and concealed. Therefore, the non-nuclear EMP has become a realistic threat for major infrastructures. Ensuring the electromagnetic safety of major infrastructures is an urgent strategic task for China. This study takes the non-nuclear EMP threat of major infrastructures as the research object, clarifies the basic concepts of and major demands for EMP protection for major infrastructures, and analyzes the measures taken by developed countries in this field from the aspects of national policies, industrial standards, fundamental research, and emergency management. In addition, the key technologies for efficient non-nuclear EMP protection are discussed, focusing on forward design, equivalent test and evaluation, and threat monitoring and prediction. Moreover, this study analyzes the current status and existing problems regarding non-nuclear EMP protection of major infrastructures in China and proposes several fundamental strategies including strengthening government leadership, promoting scientific research, formulating standards and guidance, stimulating the vitality of enterprises, conducting classified and staged implementation, boosting talent cultivation, and promoting public training. This study can provide a basic reference for the non-nuclear EMP protection of major infrastructures and further support China to handle new threats.

The regulatory framework for the protection of critical infrastructures against cyberthreats: Identifying shortcomings and addressing future challenges: The case of the health sector in particular

The concept of “Critical Infrastructures” is constantly evolving in order to reflect current concerns and to respond to new challenges, especially in terms of (cyber)security and resilience. Protection of critical infrastructures against numerous threats has therefore developed into a high priority at national and EU level. During the last two decades a new type of threat has prevailed in the Critical Infrastructure threat landscape, that of cyberattacks; Protection against them is the primary focus of this paper. In order to do so the analysis first aims to drop some light into the differences between Critical Infrastructures and Critical Information Infrastructures, terms that are often confused, and to indicate possible inadequacies in the applicable protection regulatory regime. Finally, the health sector has been chosen as a sector-specific case in an effort to demonstrate how protection of a Critical Infrastructure, challenged as it has been with a constantly increasing number of cyber incidents, could be sufficiently protected in the new digitalised era.

Continuous auditing and threat detection in multi-cloud infrastructure

Efficient change control and configuration management is imperative for addressing the emerging security threats in cloud infrastructure. These threats majorly exploit misconfiguration vulnerabilities e.g. excessive permissions, disabled logging features and publicly accessible cloud storage buckets. Traditional security tools and mechanisms are unable to effectively and continuously track changes in cloud infrastructure owing to transience and unpredictability of cloud events. Therefore, novel tools that are proactive, agile and continuous are imperative. This article proposes CSBAuditor, a novel cloud security system that continuously monitors cloud infrastructure, to detect malicious activities and unauthorized changes. CSBAuditor leverages two concepts: state transition analysis and reconciler pattern to overcome the aforementioned security issues. Furthermore, security metrics are used to compute severity scores for detected vulnerabilities using a novel scoring system: Cloud Security Scoring System. CSBAuditor has been evaluated using various strategies including security chaos engineering (fault injection) strategies on Amazon Web Services and Google Cloud Platform. CSBAuditor effectively detects misconfigurations in real-time with a detection rate of over 98%. Also, the performance overhead is within acceptable limits.

Editorial: Flank dynamics, sector collapses, lahars, and rockfalls: analysis, monitoring, and modelling of small to large scale volcanic slope instability

Slope dynamics in volcanic environments comprise a wide spectrum of phenomena, from large lateral collapse to shallow debris remobilization, which may represent a major threat for human communities and infrastructures. Many volcanos built up from the ocean floor and large portions of the volcano edifice are submerged. In these settings, only the edifice’s summit can be investigated by terrestrial remote sensing and in-situ approaches. Growth and destruction, including tectonics and gravitational phenomena, affect entire volcano flanks and are not limited to the physical boundary of the sea level but could comprise their subaqueous parts

ADAtools: Automatic Detection and Classification of Active Deformation Areas from PSI Displacement Maps

This work describes the set of tools developed, tested, and put into production in the context of the H2020 project Multi-scale Observation and Monitoring of Railway Infrastructure Threats (MOMIT). This project, which ended in 2019, aimed to show how the use of various remote sensing techniques could help to improve the monitoring of railway infrastructures, such as tracks or bridges, and thus, consequently, improve the detection of ground instabilities and facilitate their management. Several lines of work were opened by MOMIT, but the authors of this work concentrated their efforts in the design of tools to help the detection and identification of ground movements using synthetic aperture radar interferometry (InSAR) data. The main output of this activity was a set of tools able to detect the areas labelled active deformation areas (ADA), with the highest deformation rates and to connect them to a geological or anthropogenic process. ADAtools is the name given to the aforementioned set of tools. The description of these tools includes the definition of their targets, inputs, and outputs, as well as details on how the correctness of the applications was checked and on the benchmarks showing their performance. The ADAtools include the following applications: ADAfinder, los2hv, ADAclassifier, and THEXfinder. The toolset is targeted at the analysis and interpretation of InSAR results. Ancillary information supports the semi-automatic interpretation and classification process. Two real use-cases illustrating this statement are included at the end of this paper to show the kind of results that may be obtained with the ADAtools.

5G Mobile Communication, Challenges: A Survey

The hest for technological advancement in mobile communication is due to augmentation of wireless user. The deployment of 5G mobile communication is less than 4G mobile communication due to challenges in security like cyberwarfare, espionage, critical infrastructure threats. Nevertheless, critic of neurological discomforts, tissue damage in living organisms occur in the existence of EMF radiation. Also, physical scarcity for spectral efficiency arises due to ubiquitous data traffic. Inspite of these disputes data rate, low latency, device to device communication is also a challenge. In this paper we provide a survey on radiation effects, security threats, traffic management.

Automating the Detection and Classification of Active Deformation Areas—A Sentinel-Based Toolset

The H2020 MOMIT project (Multi-scale Observation and Monitoring of railway Infrastructure Threats, http://www.momit-project.eu/) is focused on showing how remote sensing data and techniques may help to monitor railway infrastructures. One of the hazards monitored are the ground movements nearby such infrastructures. Two methodologies targeted at the detection of Active Deformation Areas (ADA) and the later classification of these using Persistent Scatterers (PS) derived from Sentinel-1 imagery had been developed prior to the start of MOMIT. Although the validity of these procedures had already been validated, no actual tools automating their execution existed—these were applied manually using Geographic Information Systems (GIS). Such a manual process was slow and error-prone due to human intervention. This work presents two new applications, developed in the context of the MOMIT project, automating the aforementioned methodologies: ADAfinder and ADAclassifier. Their goal was (1) to reduce the possibility of human errors to a minimum and (2) to increase the performance/reduce the time needed to obtain results, thus allowing more room for experimentation.

Isolation in cloud computing infrastructures: new security challenges

Cloud computing infrastructures share hardware resources among different clients, leveraging virtualization to multiplex physical resources among several self-contained execution environments such as virtual machines or Linux containers. Isolation is a core security challenge for such a computing paradigm. It may be threatened by side-channels, created due to the sharing of physical resources like processor caches, or by mechanisms implemented in the virtualization layer. Side-channel attacks (SCAs) exploit and use such leaky channels to obtain sensitive data such as kernel information. This paper aims to clarify the nature of this threat for cloud infrastructures. Current SCAs are performed locally and exploit isolation challenges of virtualized environments to retrieve sensitive information. This paper also clarifies the concept of distributed side-channel attack (DSCA). We explore how such attacks can threaten isolation of any virtualized environments such as cloud computing infrastructures. Finally, we study a set of different applicable countermeasures for attack mitigation in cloud infrastructures.

Infrastructure development and contested forest governance threaten the Leuser Ecosystem, Indonesia

The Leuser Ecosystem in northern Sumatra, Indonesia is a globally-significant landscape for biodiversity conservation and ecosystem services. It is however increasingly threatened by infrastructure development, enabled by discordant forest governance amongst central and regional governments. Here we identify these infrastructure threats and related shortcomings in forest management and conservation planning. Environment-development conflicts were mapped across the Leuser Ecosystem, in addition to unofficial and official roads as well as planned road, electrical generation, and electrical transmission developments. We find that conflicts concentrate in the Leuser Ecosystem, where many protected forests were formerly degraded and are now undergoing de facto conversion. Unofficial roads, not observed within government maps, were nearly double the length of official roads within the Leuser Ecosystem (6818 km vs 3597 km). Approximately half of all roads occurred in forests, particularly historically-exploited forests. Consequently, small forest patches and narrow forest corridors comprise 27% of the Leuser Ecosystem, and most are subject to ongoing degradation and conversion. We identify eight conservation priority areas where concentrations of road developments have produced particularly vulnerable forests. Planned infrastructure developments would directly impact these priority areas and bisect the Leuser Ecosystem, contradicting national conservation planning directives. These trends are framed by an assertive decentralisation by regional governments, aggravating the legal-administrative volatility surrounding the Leuser Ecosystem and empowering development interests. Such volatility is poised to become more common in Indonesia at a time of rapidly expanding development pressures within remaining natural environments.

Cloud infrastructure and security threats in the world of cloud computing

Cloud infrastructure and security threats in the world of cloud computing