Security Scenarios Research Articles

Spatiotemporal Change Analysis and Multi-Scenario Modeling of Ecosystem Service Values: A Case Study of the Beijing-Tianjin-Hebei Urban Agglomeration, China

Ecosystem service value (ESV) assessment is a crucial indicator of regional ecological quality and ecological management effectiveness. Ecosystem services (ES) provide the environmental foundation for human existence and social advancement. However, the future course of land use change (LUC) in urban agglomerations and its implications for human society remains uncertain, which presents a challenge to maintaining a balance between ecological service functions and regional socioeconomic growth. This paper took the Beijing-Tianjin-Hebei (BTH) urban agglomeration as an example and used the future land use simulation (FLUS) model to project the spatial distribution of land use under the natural development scenario (NDS), food security scenario (FSS), and ecological priority scenario (EPS) of BTH in 2030, 2040, and 2050. Next, the changes to ESV under various scenarios were investigated through the equivalent coefficient method. In order to make more targeted recommendations for regional development, the study also used hotspot analyses to explore the impacts of LUCs on ESV. The results showed that: (1) from 2000 to 2020, the LUC in the BTH was dramatic and mainly focused on the interconversions among the three land use categories of cropland, grassland, and built-up land. The total ESV demonstrated the tendency to decrease from CNY 386,859.89 × 106 in 2000 to CNY 371,968.78 × 106 in 2020. (2) Compared with 2020, the ESV in BTH in 2050 under the FSS loses 16,568.78 × 106 CNY, followed by the NDS (CNY 10,960.84 × 106), while the ESV under the EPS increases by CNY 9373.74 × 106. The results of the scenario simulation showed that there was significant variability in ESV under different political orientations. (3) Hotspot analysis indicated that the ESV changes were clustered in the northeastern part and the eastern coastal region of the BTH. On this basis, we identified Chengde, Beijing, Tianjin, and Zhangjiakou as the key cities to focus on and made meaningful suggestions for their future regional environmental protection and sustainable development. This research can serve as a guide in creating sustainable BTH development policies and offer fresh perspectives for investigating how land use patterns affect the ecological environment’s regional quality under various policy trajectories.

Fostering Creativity in Order to Develop Mental Model Templates for Planning in National Emergencies

National Emergencies set the need for quick responses and actions from all institutions that are responsible for national security and/or citizens’ health. Course of action development as part of mitigation planning in such situations, is a complex and difficult procedure because of the uncertainty of the future events, the risk associated with decisions for human actions and the time pressure because of the emergency. This means that planning for addressing national emergencies is not a common and simple task. Moreover, because emergencies appear seldom, most of the stakeholders have lack of experience. Thus, they need more time to assimilate the incoming information into their mission planning process and finally to propose the appropriate courses of actions. An antidote to the above difficulties can be the appropriate training in combination with the enhancement of the creativity of the stakeholders. In order to address that, we propose a novel methodology of creating mind map templates, based on creativity techniques, for specific operations that can be applied to national emergencies. Our methodology, called “MEMOS”, was developed according to the relative theoretical frameworks and then tested with a case study, including hypothetical security scenarios in national emergency situations. The proposed templates can be used as inspiration tools or idea tanks in order to guide and speed up the process of course of action development in mitigation planning for similar future security operations.

Deep Learning-Based Network Security Situation Prediction for Sensor-Enabled Networks

Aims and background: Network security detection has become increasingly complex due to the proliferation of Internet nodes and the ever-changing nature of network architecture. To address this, a multi-layer feedforward neural-network has been employed to construct a model for security threat detection, which has enhanced network security protection. Objectives and Methods: Improving prediction accuracy and real-time performance, this research suggests an optimal strategy based on Clockwork Recurrent-Neural-Networks(CW-RNNs) to handle nonlinearity and temporal dynamics in network security circumstances. We get the model to pick up on both the short-term and long-term temporal aspects of network-security situations by using the clock-cycle RNN. To further improve the network security scenario prediction model, we tune the network hyperparameters using the Grey-Wolf-Optimization(GWO) technique. By incorporating a clock-cycle for hidden units, the model can improve its pattern recognition capabilities by learning short-term knowledge from high-frequency update modules and preserving long-term memory from low-frequency update modules. Results: The optimized clock-cycle RNN achieves better prediction accuracy than competing network models when it comes to extracting nonlinear and temporal characteristics of network security scenarios, according to the experimental data. Conclusions: In addition, our method is perfect for tracking massive amounts of data transmitted by sensor networks because of its minimal time complexity and outstanding real-time performance.

Iterative algorithms for solving one-sided partially observable stochastic shortest path games

Real-world scenarios often involve dynamic interactions among competing agents, where decisions are made considering actions taken by others. These situations can be modeled as partially observable stochastic games (POSGs), with zero-sum variants capturing strictly competitive interactions (e.g., security scenarios). While such models address a broad range of problems, they commonly focus on infinite-horizon scenarios with discounted-sum objectives. Using the discounted-sum objective, however, can lead to suboptimal solutions in cases where the length of the interaction does not directly affect the gained rewards of the players.We thus focus on games with undiscounted objective and an indefinite horizon where every realization of the game is guaranteed to terminate after some unspecified number of turns. To manage the computational complexity of solving POSGs in general, we restrict to games with one-sided partial observability where only one player has imperfect information while their opponent is provided with full information about the current situation. We introduce two novel algorithms based on the heuristic search value iteration (HSVI) algorithm that iteratively solve sequences of easier-to-solve approximations of the game using fundamentally different approaches for constructing the sequences: (1) in GoalHorizon, the game approximations are based on a limited number of turns in which players can change their actions, (2) in GoalDiscount, the game approximations are constructed using an increasing discount factor. We provide theoretical qualitative guarantees for algorithms, and we also experimentally demonstrate that these algorithms are able to find near-optimal solutions on pursuit-evasion games and a game modeling privilege escalation problem from computer security.

Identification of Security Scenarios in offshore Oil&Gas production facilities based on past incident analysis

Offshore Oil&Gas installations may be attractive targets of intentional attacks due to their presence in socio-political sensitive areas, their economic significance, and the high impact of scenarios that can be triggered by the mismanagement of the hazardous substances handled (e.g., crude oil, natural gas, etc.). In the present study, a new dataset including 112 past security-related incidents that affected the offshore Oil&Gas sector in the last decades was developed and analyzed using Exploratory Data Analysis (EDA), Root Cause Analysis (RCA), and Attack Tree Analysis (ATA). Significant differences were found in the types of threats and attack modes, with terrorism identified as the primary threat, followed by robbery and kidnapping. The identified scenarios caused fatalities, injuries, and asset damages at the targeted facilities, underscoring their potential to cause severe impacts, comparable to the outcomes of safety-related accidents. Tables linking identified attack modes, scenarios, and consequences specific contribution to the SVA workflow proposed by the API Recommended Practices 70 and 70I were obtained. Overall, the results obtained support the application of suitable Security Vulnerability/Risk Assessment (SVA/SRA) methodologies concerning the identification and characterization of adversaries, the definition of attack modes, the evaluation of effectiveness of existing security barriers, and the assessment of consequences.

Vulnerability extraction and prediction method based on improved information gain algorithm.

More and more attention has been paid to computer security, and its vulnerabilities urgently need more sensitive solutions. Due to the incomplete data of most vulnerability libraries, it is difficult to obtain pre-permission and post-permission of vulnerabilities, and construct vulnerability exploitation chains, so it cannot to respond to vulnerabilities in time. Therefore, a vulnerability extraction and prediction method based on improved information gain algorithm is proposed. Considering the accuracy and response speed of deep neural network, deep neural network is adopted as the basic framework. The Dropout method effectively reduces overfitting in the case of incomplete data, thus improving the ability to extract and predict vulnerabilities. These experiments confirmed that the excellent F1 and Recall of the improved method reached 0.972 and 0.968, respectively. Compared to the function fingerprints vulnerability detection method and K-nearest neighbor algorithm, the convergence is better. Its response time is 0.12 seconds, which is excellent. To ensure the reliability and validity of the proposed method in the face of missing data, the reliability and validity of Mask test are verified. The false negative rate was 0.3% and the false positive rate was 0.6%. The prediction accuracy of this method for existing permissions reached 97.9%, and it can adapt to the development of permissions more actively, so as to deal with practical challenges. In this way, companies can detect and discover vulnerabilities earlier. In security repair, this method can effectively improve the repair speed and reduce the response time. The prediction accuracy of post-existence permission reaches 96.8%, indicating that this method can significantly improve the speed and efficiency of vulnerability response, and strengthen the understanding and construction of vulnerability exploitation chain. The prediction of the posterior permission can reduce the attack surface of the vulnerability, thus reducing the risk of breach, speeding up the detection of the vulnerability, and ensuring the timely implementation of security measures. This model can be applied to public network security and application security scenarios in the field of computer security, as well as personal computer security and enterprise cloud server security. In addition, the model can also be used to analyze attack paths and security gaps after security accidents. However, the prediction of post-permissions is susceptible to dynamic environments and relies heavily on the updated guidance of security policy rules. This method can improve the accuracy of vulnerability extraction and prediction, quickly identify and respond to security vulnerabilities, shorten the window period of vulnerability exploitation, effectively reduce security risks, and improve the overall network security defense capability. Through the application of this model, the occurrence frequency of security vulnerability time is reduced effectively, and the repair time of vulnerability is shortened.

Multifunctional evaluation and multiscenario regulation of non-grain farmlands from the grain security perspective: Evidence from the Wuhan Metropolitan Area, China

Non-grain production (NGP) represents a complex social phenomenon driven by a range of interests that involve agricultural development, livelihood security, and ecological preservation. It significantly alters the structure of agricultural production and vegetation cover, influencing regional grain supply and the multifunctional composition of agriculture. To achieve sustainable development goals related to grain supply and ecological stability, this study proposes a research framework for multifunctional evaluation and multiscenario regulation of non-grain farmlands (NGFs). First, NGFs in the Wuhan metropolitan area (WHM) were extracted based on land survey data to carry out multifunctional evaluation of NGFs, including production function, ecological functions and recreational function. Second, the SOM+K-means algorithm is used to identify and explore the multifunctional bundle characteristics of NGFs to determine the regulation priority order of NGFs. Finally, a multiscenario simulation of the rigid control scale and layout arrangement of NGFs from the grain security perspective is carried out. The research results show that (1) there are 1294.34 thousand ha of NGFs in the WHM, mainly of grain and non-grain rotation type (52.36 %). The production function is higher in the central and western Jianghan Plain areas than in the northeastern and southern hilly mountainous areas; the ecological functions are the opposite; the high-value areas of recreational function are mainly located around the built-up areas of the city. (2) Multifunctional bundles divide NGFs into seven categories and determine the order of regulation based on the principle of "production priority, functional diversity". (3) By setting different grain security scenarios, it is predicted that the rigid control scale of NGFs in the WHM in 2030 is 256.08–797.55 thousand ha. Divide and restore grain production in order of the priority zone for grain production, the reserve zone for grain production, the resilience zone for grain production, the ecological improvement zone and the agricultural improvement zone, so as to achieve a regulatory layout of strict control and adaptable regulation of NGFs. The research supports the regulation of NGFs in balancing grain security and ecological stability to promote sustainable agricultural production.

Security experimental framework of trajectory planning for autonomous vehicles

In the contemporary landscape, autonomous vehicles (AVs) have emerged as a prominent technological advancement globally. Despite their widespread adoption, significant hurdles remain, with security standing out as a critical concern. The potential for attacks within AV networks, exemplified by the Trajectory Privacy Attack on Autonomous Driving (T-PAAD), underscores the urgency for robust security measures. Unfortunately, existing simulations for preemptively assessing the T-PAAD attack's impact are scarce. This paper introduces the Security Experimental Framework for Autonomous Vehicles (SEFAV), designed to address this gap by providing a versatile platform for simulating security scenarios in AV environments. SEFAV is cross-platform and compatible with different operating systems such as Windows and Linux, enhancing accessibility for researchers and practitioners. Our primary focus lies in showcasing the T-PAAD attack within our framework, highlighting its efficacy in evaluating and fortifying AV security.

Solutions for Mobile Devices Security Concerns, Aden Refinery Company (A.R.C) Case Study

Companies that allow their employees to use mobile devices for work purpose, they suffer from cybersecurity challenges such as virus or malware, download illegal content, poor authorization and authentication, etc. These challenges need solutions or controls to solve or mitigate them. In this paper, there are two objectives. The first aim is to know which of the security controls were asked in the questionnaire used in the A.R.C. The second objective is to know which security scenarios the IT department uses to protect the information from potential mobile device security concerns. A descriptive methodology was employed in this study. Data were collected from forty people in the IT department at Aden Refinery Company through a questionnaire. The results show that some security controls, such as antivirus/ antimalware, backups, firewalls, and policies, are used in the A.R.C.

Rainfall analysis in Xingu River Basin based on the 1981-2020 timeframe

It is essential understanding the incidence of hydrometeorological variables in each river basin to help planning actions focused on guaranteeing multiple water uses, environmental preservation and prevention measures taken against extreme events. The aim of the current study is to investigate rainfall trends in Xingu River Hydrographic Basin (XRHB) from 1981 to 2020. Data propensity analysis was carried out based on the Mann-Kendall methods, on five-year moving average graph, as well as on variations in Climatological Normals (CN) recorded from 1981 to 2010 and from 1991 to 2020. Moreover, the Log-Normal theoretical distribution was adjusted to show mean rainfall predictions within 10-to-100-year return time (Rt). It was done by applying the Kolmogorov-Smirnov (KS), Chi-Square (?2), Filliben (Fi) and Anderson-Darling (AD) tests. Results have evidenced historical series with no trend and minimal variation (-0.5%) between the two CN intervals. Estimates within a 10-to-100-year RT scenario have evidenced rainfall rates higher than the historical average (HA) ranging from 10.0% to 20.0%. Thus, XRHB is a promising scenario for water security planning purposes, since it meets the fundamentals of Brazilian environmental and water resources laws, as well as target 6.5 of Sustainable Development Goal 6 (SDG 6 - UN), which deals with implementing integrated water-resources management at global level.

The importance of natural land carbon sinks in modelling future emissions pathways and assessing individual country progress towards net-zero emissions targets

Nature-based solutions (NBS), in the form of active ecosystem conservation, restoration and improved land management, represent a pathway to accelerate net-zero emissions (NZE) strategies and support biodiversity. Meaningful implementation and successful accounting depend on the ability to differentiate between anthropogenic and natural carbon fluxes on land. The United Nations Framework Convention on Climate Change (UNFCCC) land carbon accounting methods currently incorporate all CO2 fluxes on managed land in country inventories without distinguishing between anthropogenic and natural components. Meanwhile, natural land carbon sinks are modelled by earth system models but are mostly reported at global level. Here we present a simple yet novel methodology to estimate the present and future progression of natural land sinks at the country and regional level. Forests dominate the uptake of CO2 on land and as such, our analysis is based on allocating global projections of the natural land carbon flux to individual countries using a compilation of forest land areas for a historic and scenario range spanning 1960–2100. Specifically, we use MIT’s carbon cycle model simulations that are set in the context of emissions pathways from the Shell Energy Security Scenarios (2023). Our natural land carbon flux estimates for individual countries and regions such as the European Union (EU) show generally good agreement with independent estimates from recent land-use harmonisation studies for 2000–2020. Hence, our approach may also provide a simple, first-order exploration of future natural land fluxes at country level—a potential that other studies do not yet offer. In turn, this enables better understanding of the anthropogenic and natural components contributing to country NZE targets under different scenarios. Nevertheless, our findings also suggest that models such as the Shell World Energy Model (WEM) would benefit from further improvements in the apportionment of land carbon sources and sinks to evaluate detailed actions to meet country targets. More importantly, uncertainties remain regarding the resilience of land ecosystems and their capacity to store increasing amounts of carbon under progressive global warming. Therefore, we recommend that the carbon cycle modelling and energy modelling research communities continue to collaborate to develop a next generation of relevant data products to distinguish anthropogenic from natural impacts at local, regional and national levels.

Driving Analysis and Multi Scenario Simulation of Ecosystem Carbon Storage Changes Based on the InVEST-PLUS Coupling Model: A Case Study of Jianli City in the Jianghan Plain of China

The carbon storage capacity of terrestrial ecosystems serves as a crucial metric for assessing ecosystem health and their resilience to climate change. By evaluating the effects of land use alterations on this storage, carbon management strategies can be improved, thereby promoting carbon reduction and sequestration. While county-level cities are pivotal to ecological conservation and high-quality development, they often face developmental challenges. Striking a balance between economic growth and meeting peak carbon emissions and carbon neutrality objectives is particularly challenging. Consequently, there is an urgent need to bolster research into carbon storage management. The study focuses on Jianli City, employing the InVEST model and land use data to examine the response patterns of land use changes and terrestrial system carbon storage from 2000 to 2020. Using the PLUS model, the study simulated the land use and carbon storage in Jianli City for the year 2035 under three scenarios: Natural Development scenario, Urban Expansion scenario, and Ecology and food security scenario. Our findings indicate the following: (1) Between 2000 and 2020, significant shifts in land use were observed in Jianli City. These changes predominantly manifested as the interchange between Cropland and Water areas and the enlargement of impervious surfaces, leading to a decrease of 691,790.27 Mg in carbon storage. (2) Under the proposed scenarios—Natural Development scenario, Urban Expansion scenario, and Ecology and food security scenario—the estimated carbon storage capacities in Jianli City were 39.95 Tg, 39.90 Tg, and 40.14 Tg, respectively. When compared with the 2020 data, all these estimates showed an increase. In essence, our study offers insights into optimizing land use structures from a carbon storage standpoint to ensure stability in Jianli’s carbon storage levels while mitigating the risks associated with carbon fixation. This has profound implications for the harmonious evolution of regional eco-economies.

EU space security-An 8-Step online discourse analysis to decode hybrid threats.

Space security has emerged as a concern for the European Union (EU), given that space systems have become integral to ensuring the safety of all European society. This strategy reflects the interaction of geopolitical dynamics and the rising specter of hybrid threats. However, grappling with hybrid threats targeting the EU space presents distinct challenges, primarily owing to their elusive nature. Hence, our objective is to develop practical methodologies to identify and mitigate such threats effectively. To meet this objective, we propose an innovative 8-step process. This approach streamlines the systematic identification and analysis of discourses of hybrid threats within online platforms dedicated to EU space discussions. Our methodology underwent rigorous scrutiny, including a comprehensive literature review that prioritized peer-reviewed manuscripts from sources such as Web of Science and Elsevier Scopus. Additionally, we selected documents from Google Scholar to ensure comprehensive coverage of diverse scholarly contributions, enriching the depth of our analysis. Our research yielded a conceptual framework for Online Discourse Analysis (ODA) tailored to evaluate hybrid threats targeting EU space defense and security. The results highlight the importance of leveraging advanced ODA techniques to deepen our understanding of emerging threats. In conclusion, we advocate for the adoption of these innovative methods to enhance the robustness of EU space defense strategies in the face of evolving security scenarios. The establishment of the 8-step ODA framework marks a pivotal milestone, offering a structured approach to deciphering hybrid threats. Looking ahead, we aim to empirically validate this framework by creating and deploying custom-tailored software designed to identify potential hybrid threats jeopardizing the security of European space assets. Through continued research and practical implementation, we endeavor to fortify the EU's defenses against emerging threats in the space domain.

Construction of Ecological Security Patterns and Evaluation of Ecological Network Stability under Multi-Scenario Simulation: A Case Study in Desert–Oasis Area of the Yellow River Basin, China

Land use change has a significant impact on the sustainability of ecosystems, and ecological security patterns (ESPs) can improve environmental quality through spatial planning. This study explored a multi-scenario ESP framework by integrating future land use simulation (FLUS) and minimum cumulative resistance (MCR) for urban agglomeration along the Yellow River Basin (YRB) in Ningxia. The research involved simulating land use change in 2035 under four development scenarios, identifying ecological security networks, and evaluating network stability for each scenario. The study revealed that the ecological sources under different development scenarios, including a natural development scenario (NDS), an economic development scenario (EDS), a food security scenario (FSS), and an ecological protection scenario (EPS), were 834.82 km2, 715.46 km2, 785.56 km2, and 1091.43 km2, respectively. The overall connectivity values (OG) for these scenarios were 0.351, 0.466, 0.334, and 0.520, respectively. It was found that under an EPS, the ESPs had the largest area of ecological sources and the most stable ecological network structure, which can effectively protect natural habitats. This study provides a valuable method for identifying ESPs that can respond to diversity and the uncertainty of future development. It can assist decision-makers in enhancing the ecological quality of the study area while considering various development scenarios.

Exploring on Cloud Security Landscapes:A Comprehensive Review

Nowadays, the massive growth of data and the adoption of the cloud-based architecture indicate that the secure storage of data is a very essential issue. Cloud storage is the solution to convenient, scalable and cost-effective storage, yet security issues persist, especially regarding the protection of sensitive information. The encryption plays a vital role in which data is made unreadable and only accessible by those with the proper authorization. This helps in data management and minimizes data leaks. Shared responsibility in cloud security entails collaboration between the operators and users through the encryption strategy and a number of security measures to protect the data, apps, and infrastructure. This paper deals with the different cryptographic techniques and technologies used for improving cloud security. It discusses the notion of Non-Deterministic Cryptographic Scheme (NCS), which is a secure technique that effectively utilizes randomness in cryptographic algorithms. Regarding the Intel architecture compatibility, SGX and blockchain technology implementation not only ensures data immutability, but it also strengthens the data against cyber threats. SGX produces secure compartments inside cloud platforms, while blockchain makes data integrity possible through decentralized and immutable ledger technology. The research gives an impression of the seaside and multi sided cloud security. Involving encryption, access control, data loss prevention, and intrusion detection systems. Technologies of cryptography like Group Key Based Attribute Encryption (GKBAE) and Modified Random Fibonacci Cryptography (MRFC) offers both data protection and reduced resource requirement in clouds. GKBAE enables fine-grained access control and attribute-based encryption that ensures high data security without any additional computation. In addition, the paper also talks about the privacy-preserving technology, and policy-oriented access control framework. Techniques like Linear Elliptical Curve Digital Signature (LECDS) that combine digital signatures with blockchain technologies can also be used to implement the above features. To sum up, the changing scenario of cloud security requires ceaseless innovation and application of the latest cryptographic methods. Through encryption, hardware level security features and distributed ledger technology implementation, data environment is more strengthened and vulnerabilities to hacking, data leakage, and unauthorized access are decreased. Making use of the modern security tools not only does the data safety but also is responsible for the trustworthiness and stability of the cloud computing infrastructure. Keywords— Cloud Security, Encryption, Data Protection, Non-Deterministic Cryptographic Scheme (NCS), Intel SGX, Blockchain, Group Key Based Attribute Encryption (GKBAE), Modified Random Fibonacci Cryptography (MRFC), Data Confidentiality.

Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network

Smart Car's era ushers in new challenges, some of which are in the field of information security such as wise cyber-attack prevention. Traditional IDS systems would lose everything in such a dynamic environment with their centralized architectures, and this approach could create single points of failure and privacy issues. Along with interconnectedness, cyber security becomes an inevitable problem as smart vehicles are incorporated into a daily life. Traditional security mechanisms usually lack scalability and privacy, which brings about the need to develop alter-nate or innovative methods. This research demonstrates a mixed security system that combines both federated learning and blockchain technologies to improve intrusion detection in smart vehicular networks. We evaluated the effectiveness of this framework using four machine learn-ing models as respectively; Support Vector Machine (SVM), Decision Tree, Neural Network, and Random Forest. Empirical results show that SVM had the highest accuracy of both 93.88% in training and 91.84% in validation, which is higher than Decision Tree, Neural Network, and Random Forest models. These findings evidently demonstrate that the federated learning and blockchain are a strong solution for the plausible security of smart vehicle networks; with SVM being employed mostly in complex security scenarios. DOI: https://doi.org/10.52783/pst.353

Ammonia as a Potential Energy Vector in the Burgeoning Hydrogen Economy

AbstractThe adoption of green hydrogen economy is an indispensable necessity in the current global scenario of environment and energy security. In this endeavor, ammonia is poised to play a key vector of hydrogen to mitigate the challenges arising from transportation, storage and safety. Besides containing a high volumetric and gravimetric hydrogen density, NH3 decomposition into H2 for onsite utilization as a distributed energy source is devoid of greenhouse gases production. In this endeavor, significant technological advancements have been made for in situ production of H2 from NH3 decomposition and the use of NH3 in fuel cell devices to produce electricity. The ammonia decomposition methods to produce H2 mainly involve thermocatalytic, oxidative, electrocatalytic and photocatalytic, among which the catalyst assisted thermal cracking of NH3 has been widely investigated. The research progress in electrolysis of NH3 has been notable in the last couple of years and provides a low‐cost alternative to produce H2 at room temperature. In the area of device development, solid oxide fuel cells (SOFC) have witnessed rapid development in the performances and stability, as ammonia is completely decomposed into H2 and N2 at high operating temperature above ~700 °C.

NRPerson: A Non-Registered Multi-Modal Benchmark for Tiny Person Detection and Localization

In recent years, the detection and localization of tiny persons have garnered significant attention due to their critical applications in various surveillance and security scenarios. Traditional multi-modal methods predominantly rely on well-registered image pairs, necessitating the use of sophisticated sensors and extensive manual effort for registration, which restricts their practical utility in dynamic, real-world environments. Addressing this gap, this paper introduces a novel non-registered multi-modal benchmark named NRPerson, specifically designed to advance the field of tiny person detection and localization by accommodating the complexities of real-world scenarios. The NRPerson dataset comprises 8548 RGB-IR image pairs, meticulously collected and filtered from 22 video sequences, enriched with 889,207 high-quality annotations that have been manually verified for accuracy. Utilizing NRPerson, we evaluate several leading detection and localization models across both mono-modal and non-registered multi-modal frameworks. Furthermore, we develop a comprehensive set of natural multi-modal baselines for the innovative non-registered track, aiming to enhance the detection and localization of unregistered multi-modal data using a cohesive and generalized approach. This benchmark is poised to facilitate significant strides in the practical deployment of detection and localization technologies by mitigating the reliance on stringent registration requirements.

Predicting DoS and DDoS attacks in network security scenarios using a hybrid deep learning model

Abstract Network security faces increasing threats from denial of service (DoS) and distributed denial of service (DDoS) attacks. The current solutions have not been able to predict and mitigate these threats with enough accuracy. A novel and effective solution for predicting DoS and DDoS attacks in network security scenarios is presented in this work by employing an effective model, called CNN-LSTM-XGBoost, which is an innovative hybrid approach designed for intrusion detection in network security. The system is applied and analyzed to three datasets: CICIDS-001, CIC-IDS2017, and CIC-IDS2018. We preprocess the data by removing null and duplicate data, handling imbalanced data, and selecting the most relevant features using correlation-based feature selection. The system is evaluated using accuracy, precision, F1 score, and recall. The system achieves a higher accuracy of 98.3% for CICIDS-001, 99.2% for CICIDS2017, and 99.3% for CIC-ID2018, compared to other existing algorithms. The system also reduces the overfitting of the model using the most important features. This study shows that the proposed system is an effective and efficient solution for network attack detection and classification.

Evaluating the Use of Hypothetical 'Would You Rather' Scenarios to Discuss Privacy and Security Concepts with Children

Children are exposed to technology at home and school at very young ages, often using family mobile devices and educational apps. It is therefore critical that they begin learning about privacy and security concepts during their elementary school years, rather than waiting until they are older. Such skills will help children navigate an increasingly connected world and develop agency over their personal data, online interactions, and online security. In this paper, we explore how a simple technique---a ''Would Your Rather'' (WYR) game involving hypothetical privacy and security scenarios---can support children in working through the nuances of these types of situations and how educators can leverage this approach to support children's privacy and security learning. We conducted three focus groups with 21 children aged 7-12 using the WYR activity and interviewed 13 elementary school teachers about the use of WYR for facilitating privacy and security learning. We found that WYR provided a meaningful opportunity for children to assess privacy and security risks, consider some of the social and emotional aspects of privacy and security dilemmas, and assert their agency in a manner typically unavailable to children in an adult-centric society. Teachers highlighted connections between privacy and security dilemmas and children's social and emotional learning and offered additional insights about using this WYR technique in and beyond their classrooms. Based on these findings, we highlight four opportunities for using WYR to support children in engaging with privacy and security concepts from an early age.