Security Decisions Research Articles

SecKG2vec: A Novel Security Knowledge Graph Relational Reasoning Method based on Semantic and Structural Fusion Embedding

Knowledge graph technology is widely used in network security design, analysis, and detection. By collecting, organizing, and mining various security knowledge, it provides scientific support for security decisions. Some public Security Knowledge Repositories (SKRs) are frequently used to construct security knowledge graphs. The quality of SKRs affects the efficiency and effectiveness of security analysis. However, the current situation is that the identification of relational information among security knowledge elements is not sufficient and timely, and a large number of key relational information is missing. In view of this, we propose a security knowledge graph relational reasoning method, based on the fusion embedding of semantic correlation and structure correlation, named SecKG2vec. By SecKG2vec, the embedded vector simultaneously presents both semantic and structural characteristics, and it can exhibit better relational reasoning performance. In qualitative evaluation and quantitative experiments with baseline methods, SecKG2vec has better performance in relationship reasoning task and entity reasoning task, and potential capability of 0-shot scenario prediction.

We need to aim at the top: Factors associated with cybersecurity awareness of cyber and information security decision-makers.

Cyberattacks pose a significant business risk to organizations. Although there is ample literature focusing on why people pose a major risk to organizational cybersecurity and how to deal with it, there is surprisingly little we know about cyber and information security decision-makers who are essentially the people in charge of setting up and maintaining organizational cybersecurity. In this paper, we study cybersecurity awareness of cyber and information security decision-makers, and investigate factors associated with it. We conducted an online survey among Slovenian cyber and information security decision-makers (N = 283) to (1) determine whether their cybersecurity awareness is associated with adoption of antimalware solutions in their organizations, and (2) explore which organizational factors and personal characteristics are associated with their cybersecurity awareness. Our findings indicate that awareness of well-known threats and solutions seems to be quite low for individuals in decision-making roles. They also provide insights into which threats (e.g., distributed denial-of-service (DDoS) attacks, botnets, industrial espionage, and phishing) and solutions (e.g., security operation center (SOC), advanced antimalware solutions with endpoint detection and response (EDR)/extended detection and response (XDR) capabilities, organizational critical infrastructure access control, centralized device management, multi-factor authentication, centralized management of software updates, and remote data deletion on lost or stolen devices) are cyber and information security decision-makers the least aware of. We uncovered that awareness of certain threats and solutions is positively associated with either adoption of advanced antimalware solutions with EDR/XDR capabilities or adoption of SOC. Additionally, we identified significant organizational factors (organizational role type) and personal characteristics (gender, age, experience with information security and experience with information technology (IT)) related to cybersecurity awareness of cyber and information security decision-makers. Organization size and formal education were not significant. These results offer insights that can be leveraged in targeted cybersecurity training tailored to the needs of groups of cyber and information security decision-makers based on these key factors.

A Comprehensive Security Risk Assesment Framework SRAF_IoV for Internet of Vehicles.

The advancement in digital world have led to the development of smart vehicles that connect to the Internet of Vehicles (IoV) Infrastructure. These smart vehicle also connects and communicate with other vehicles, and their connected components. Once connected, they can share information such as speed, direction, and distance between vehicles.IoV enhances traffic infrastructure by reducing congestion and incidents, ultimately improving road safety within highways and metropolitan cities. However, when smart vehicles use IoV to share data through vehicle-to-vehicle (V2V), Vehicle to Infrastructure (V2I), and Vehicle to everything (V2X) they also introduce security risks and vulnerabilities to the IoV Infrastructure. Malicious actors can exploit these vulnerabilities to alter data within the connected units of IoV infrastructure, such as electronic control units, onboard units, control networks, and sensors. Securing data within the IoV ecosystem is crucial to prevent malicious activity that could compromise confidentiality, integrity, and availability. In this study, a security risk assessment framework for IoV (SRAF-IoV) has been developed to identify vulnerabilities within the IoV network. SRAF-IoV that assesses security risks in connected vehicles. It evaluates vulnerabilities and assigns a risk level based on different communication channels (V2V, V2I, and V2X). The proposed framework was tested against various cyber-attacks to determine risk levels associated with different attack types (low, medium, high, and critical). The performance of SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV can push the smart vehicle industry to make critical security decisions during the development of smart vehicles.

Provably Secure Decisions Based on Potentially Malicious Information

Provably Secure Decisions Based on Potentially Malicious Information

Long-Term Variation Patterns of Precipitations Driven by Climate Change in China from 1901 to 2022

Studying long-term precipitation trends is crucial for sustainable development, as the proper utilization of water resources is essential for maintaining a sustainable water supply. The objective and novelty of this paper was to reveal the gradual mutation process of precipitation in China over a century. This study utilized monthly precipitation data from 1901 to 2022 (at a century scale) to analyze and explore the spatiotemporal variability in precipitation across different time scales and regions with a trend analysis, an abrupt change analysis, and gravity center models. The findings indicate that (1) from 1901 to 2022, the precipitation in China generally decreased from the southeast coastal areas toward the northwest inland regions. (2) There were significant differences in the migration of precipitation gravity centers among the different study regions, with the least dispersion being observed in the Liao River basin, while the Hai River basin, various river basins in the northwest, and the Pearl River basin exhibited certain regularities in gravity center movement, and other regions showed periodic variations. (3) Over the period from 1901 to 2022, there was a trend of transitioning from lower to higher precipitation levels. (4) According to continuous long-term abrupt change tests, the timing of precipitation shifts varied across different basins. Precipitation, as a crucial component of natural resources, directly impacts various aspects of socio-economic life. Research findings provide decision support for regional flood control and disaster reduction and offer scientific decisions for ecological security.

Information security decisions of security‐interdependent firms in the presence of consumer sensitivity

AbstractFirms suffer security‐interdependent risks while applying network technology, causing severe customer churn. This paper studies the security and price decisions of security‐interdependent firms that face security‐sensitivity consumers. We show that one firm's product price increases (decreases) with the other's security efforts under positive (negative) interdependence. Firms can overinvest or underinvest in security due to security interdependence. Two mechanisms are proposed to solve this distortion. In the Reward mechanism, the firms reward (penalize) the other under the positive (negative) interdependence. In the Outsourcing mechanism, the security service providers raise (reduce) the compensations with consumer sensitivity.

Information Security Research in the Information Systems Discipline: A Thematic Review and Future Research Directions

Information security continues to grow in importance in all aspects of society and therefore evolves as a prevalent research area. The information systems (IS) discipline offers a unique perspective to move this stream of literature forward. Using a semiautomated thematic analysis approach based on the topic modeling technique, we review a broad range of information security literature to investigate how we may integrate and advance our understanding of information security. Our analysis reveals four major themes, including information security policy (ISP) compliance, motivations and susceptibility, software security decisions, and firm security strategy. We also identify a theme of security in broader contexts, in which studies consider the societal impacts of information security and online hacker behavior. This review contributes to IS literature by 1) synthesizing the broad range of information security research published in the top journals of the field, moving beyond prior reviews focusing on a narrower scope such as individual ISP compliance; 2) identifying major themes and proposing an overarching research framework encompassing these themes and their interconnections, allowing us to envision future research directions; and 3) enumerating a semiautomated topic modeling approach that other researchers can employ.

Modeling risk potential fields for mandatory lane changes in intelligent connected vehicle environment

In the environment of Intelligent Connected Vehicles (ICVs), the ICV system is capable of gathering, processing, and transmitting the majority of traffic information to each vehicle, thereby empowering drivers to make more informed and secure decisions. Nevertheless, ICVs are still incapable of completely avoiding traffic conflicts and accidents. Consequently, developing a risk assessment model for ICVs is crucial for their deployment. To investigate the dynamic patterns of lane-changing vehicle risks within ICV environments, this study developed a quantitative relationship model based on the Artificial Potential Field (APF) theory. The model correlates the Total Potential Field (TPF) of mandatory lane-changing (MLC) vehicle with the longitudinal/transverse gravitational potential field (LGPF/TGPF) and the longitudinal/transverse repulsion potential field (LRPF/TRPF). Subsequently, three sets of Human Machine Interface (HMI) systems were developed to delineate distinct ICV environments: baseline, warning group, and guidance group. The baseline furnishes fundamental data, the warning group supplements preceding vehicle icons and real-time headway information, whereas the guidance group additionally augments speed and voice guidance functionalities. Following this, a simulated driving experiment involving 43 participants was carried out to simulate MLC scenarios in highway merging sections. Using enumeration and gradient descent methods, the quantitative relationship between different types of risk fields was determined. The findings indicated that the ICV environment significantly impacts the mean values of LGPF, TGPF, LRPF (Leading Vehicle, LV), and TRPF, while insignificantly affecting the mean value of LRPF (Following Vehicle, FV). Nevertheless, a notable discrepancy in the mean value of LRPF (FV) was observed between the warning and guidance groups. Comparative results with lane positions demonstrate that the proposed TPF accurately depicts vehicle lane-changing behaviors. Comparative results with classical risk indicators indicate that TPF more accurately depicts the actual driving risks and variation trends faced by vehicles in different MLC processes. The research findings will be pivotal in improving real-time control and guidance strategies for vehicles in highway merging sections under ICV environments.

An international relations quantitative evaluation model (IRQEM) based on an integrated method.

International relations (IR) have great uncertainty and instability. Bad IR or conflicts will bring about heavy economic losses and widespread social unrest domestically and internationally. The accurate prediction for bilateral relations can support decision making for timely responses, which will be used to find ways to maintain development in the complex international situation. An international relations quantitative evaluation model (IRQEM) is proposed by integrating a variety of research models and methods like the interpretative structural modeling method (ISM), Bayesian network (BN) model, the Bayesian search (BS), and the expectation-maximization (EM) algorithm, which is novel for IR research. Factors from several different fields are identified as BN nodes. Each node is assigned different state values. The hierarchical structure of these BN nodes is obtained by ISM. The data collection of 192 cases is used to construct the BN model by GeNIe 4.0. The IRQEM can be used to evaluate the influence of emergencies on IR. The critical factors of IR also can be explored through our proposed model. Results show that the prediction of bilateral relations under emergencies can be realized by updating the indicator set when emergencies occur. The capability to anticipate threats of IR changes is advanced by optimizing the reporting information of IR forecasting through a combination of qualitative and quantitative methods, charts, and texts. Relevant analysis results can provide support for national security decision making.

The Impact of Management Information Systems on Security Decision-Making : An Analytical Research for a Sample of Security Services Officers at Al-Muthana Governorate

The current research aims to identify the extent of the impact of management information systems on the level of security decision-making among a sample of security services officers in Muthana Governorate, and the researcher used the descriptive analytical approach to present, analyze and then explain the information, and collect data through the questionnaire as a main tool, designed based on international standards after making amendments to its paragraphs consisting of (40) paragraphs that included the two research variables, as well as through the field coexistence of the researcher and personal interviews, and the questionnaire was distributed to all research community ( Comprehensive inventory) by (200) forms from officers working in the various directorates of security services in (Muthana Governorate) to retrieve (197) forms, valid for statistical analysis, including (191) form invalid for analysis (6) form, and non-retrieved (3) form, has been using a set of statistical methods and represented by the program (AmosV23 In some statistical aspects, the ready-made statistician (in a sample of security services officers in Muthana Governorate) in addition to the researcher's adoption of statistical methods (arithmetic averages, standard deviation, coefficient of differences, relative importance), the research has reached the most prominent result represented in: There is interest in improving the level of management information systems because of their role in making the finest security decisions in the directorates of the security services in Muthana Governorate.

Person-centred data sharing: Empirical studies in private individuals’ attitudes

Background Recognising the power of data analytics, researchers are anxious to gain access to personal data either directly from data subjects or via research data sets. This requires a secure environment, such as a trusted research environment (TRE). However, it is unclear how the data subjects themselves regard sharing their data with TREs, especially if research goals are difficult to specify upfront or data are used for secondary purposes, making informed consent difficult to manage. We review three empirical studies to throw some light on individual attitudes to sharing health data. Methods Three anonymous, online surveys were run. The first involving 800 UK residents aimed at understanding how participants view the health data security. The second involving 500 UK residents aimed at identifying private individual views on privacy. These two surveys used a crowdsourcing platform. The third involved 1086 students at a UK university reporting their engagement with a trial diagnostic method for SARS-CoV-2. Results The first survey demonstrated that private individuals could make security decisions though they usually assume the recipient of their personal data to be responsible for all aspects of keeping the data safe. The second highlighted that individuals were aware of privacy risks but are motivated to share their data based on different contextual assumptions. The third, involving the incidental sharing of sensitive data during the SARS-CoV-2 pilot highlighted that prosocial motivations override potential personal benefit of such testing. Conclusions The three, unconnected surveys make clear that there are tensions between private individual understanding of data security and privacy risk, on the one hand, and how they behave, on the other. Respondents rely on data stewards to keep their data safe, though are likely to share even sensitive data for prosocial benefit. These findings have implications for those offering TRE services for research.

Decision Model to Design Trust-Focused and Blockchain-Based Health Data Management Applications

Many Blockchain-based approaches have been published in the field of health data management applications (HDMAs). However, no comprehensive guideline exists to guide the multiple and interdependent design decisions to develop such systems. This paper aims to support the HDMA system design processes by introducing a novel decision model. The model considers all relevant requirements, from regulatory context to user needs and trust considerations. To generate the decision model, we define a taxonomy that organizes previously published approaches by their technical design features and combines it with the trust assumptions of the participating actors according to the STRIDE method. The model aims to support a cohesive overall system design by addressing Blockchain type, off-chain storage, identity and access management, security decisions, and the specific use case of data donation. A group of experts evaluated the decision tree and its utility is demonstrated in three representative use cases. Special attention is paid to the use case of data donation via a data trustee, which is examined in detail.

Enhancing Global Food Security: Opportunities for the American Meteorological Society

Abstract Food security is a key pillar of environmental security yet remains one of the world’s greatest challenges. Its obverse, food insecurity, negatively impacts health and well-being, drives mass migration, and undermines national security and global sustainable development. Ensuring food security is a delicate balance of myriad concerns within the atmospheric and Earth sciences, agronomy and agriculture engineering, social sciences, economics, monitoring, and policymaking. A Food Security Presidential Session at the American Meteorological Society’s (AMS) 2022 Annual Meeting brought together experts across disciplines to tackle issues at the nexus of weather, climate, and food security. The starkest takeaway was the realization that, despite its importance and clear roles for the atmospheric and climate sciences, food security has not been a focus for the AMS community. The aim of this paper is to build on the perspectives shared by this expert panel and to identify overlapping issues and key points of intersection between the food-security community and AMS. We examine 1) the interactions between weather, climate, and the food system and how they influence food security; 2) the time and spatial scales of food security decision support that match weather and climate phenomena; 3) the role of both providers and users of information as well as decision-makers in improving research to operations for food security; and 4) the opportunities for the AMS community to address food security. We conclude that, moving forward, the AMS community is well-positioned to scale up its engagement across the global food system to address existing scientific needs and technology gaps to improve global food security.

Role of Gender in the Evaluation of Security Decisions

Role of Gender in the Evaluation of Security Decisions

UETOPSIS: A Data-Driven Intelligence Approach to Security Decisions for Edge Computing in Smart Cities

Despite considerable technological advances for smart cities, they still face problems such as instability of cloud server connection, insecurity during data transmission, and slight deficiencies in TCP/IP network architecture. To address such issues, we propose a data-driven intelligence approach to security decisions under Named Data Networking (NDN) architecture for edge computing, taking into consideration factors that impact device entry in smart cities, such as device performance, load, Bluetooth signal strength, and scan frequency. Despite existing techniques for Order Preference by Similarity to Ideal Solution (TOPSIS)-based on entropy weights methods are improved and applied, there exist unstable decision results. Due to this, we propose a technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-based on utility function and entropy weights, named UETOPSIS, where the corresponding utility function is applied according to the influence of each attribute on the decision, ensuring the stability of the ranking of decision results. We rely on an entropy-based weights mechanism to select a suitable master controller for the design of the multi-control protocol in the smart city system, and utilize a utility function to calculate the attribute values and then combine the normalized attribute values of utility numbers, starting by analyzing the main work of the controllers. Lastly, a prototype is developed for performance evaluation purposes. Experimental evaluation and analysis show that the proposed work has better authenticity and reliability than existing works and can reduce the workload of edge computing devices when forwarding data, with stability 24.7% higher than TOPSIS, significantly improving the performance and stability of system fault tolerance and reliability in smart cities, as the second-ranked controller can efficiently take over the work when a central controller fails or damaged.

Managing partial outsourcing on information security in the presence of security externality

To efficiently manage information security, firms typically outsource the security of partial business (core and non-core) to a managed security service provider (MSSP). Four options can be adopted for a firm on security strategy, that is, all business is managed in-house (IN Strategy), all business is outsourced to the MSSP (OA Strategy), the core business is outsourced and non-core is managed in-house (OC Strategy), and the core is managed in-house and non-core is outsourced (ONC Strategy). We consider the impact of security externality on the firm’s partial outsourcing strategies and find that if the firm wants a higher security quality of the core business, it is better to manage the core business by itself under an environment where the security externality is negative and the security loss ratio between core and non-core business is low. In addition. The security externality has different effects on both parties’ security decisions under the OC and ONC strategies. Moreover, we show the firm will adopt the partial outsourcing strategy only under a very high security externality when the security loss ratio is high, and the OC strategy is always the worst strategy when the MSSP’s cost coefficient is low. Finally, we extend the main model to an asymmetric case to make our model more general.

Cyber attacker’s next action prediction on dynamic real-time behavior model

This paper addresses the critical need for enhanced threat analysis amid escalating cyber threats. Recognizing limitations in current network-level Attack Graphs (AGs), including scalability issues and the challenge of detecting zero-day attacks, we introduce a two-phase approach. Firstly, we dynamically model the attacker’s real-time behavior within the target system, resulting in a minimal AG with action states and their system impact. In the second phase, a Hidden Markov Model (HMM) predicts the next action based on the real-time behavior model, refined through Honeypot data for continuous accuracy. The minimal AG not only predicts but also forecasts the impact of the next action, facilitating robust security decisions. Experimental evaluations using Cowrie Honeypot logs demonstrate its efficacy, outperforming conventional algorithms. This innovative approach significantly advances cyber threat analysis, enhancing security decision-making within a concise framework.

Secure decision tree classification with decentralized authorization and access control

Outsourcing decision tree classification services to the cloud is highly beneficial, yet raises critical privacy problems. In order to preserve data privacy, data owners may choose to upload encrypted data rather than raw data to the classification services. However, these solutions adopted today for encrypted data classification not only fall short in system flexibility and scalability, but also face the single point of failure problem. In this paper, we design, implement, and evaluate a secure decision tree classification scheme that allows decentralized authorization and access control service (SDTC-DAAC). Firstly, we propose a new framework that decouples data encryption and data computation logic to achieve the separation of data storage and computation, which significantly improves upon the flexibility and effectiveness, thus achieving cross-system compatibility requirements. Secondly, we present an end-to-end encrypted access control mechanism which enables authorized users from different parties to participate in calculations together. Finally, we further devise a scheme which serves decentralized storage service of data access control policies and access authorization without trusted intermediaries. Extensive property and performance analysis shows that SDTC-DAAC is effectiveness, as well as satisfying the security requirements for data privacy in an outsourcing environment.

Associative Rules for Modeling International Security Decisions in the Context of the Ukrainian-Russian War. Preliminary Evaluations

Abstract Research background By launching a war against Ukraine, Russia changed the entire world system and demonstrated the ineffectiveness of the global security system. Today it is necessary to look for effective solutions to support the adoption of security decisions and develop effective strategies for the formation of a new architecture of the international security system. Purpose The paper aims to obtain preliminary approximate estimates of the state commitments of the countries of the world that support Ukraine in the war with Russia and to reveal non-obvious connections and regularities in the provision of various types of aid. Research methodology The data were collected on the rough refugee cost estimate, quantitative assessment of the government-to-government commitments, and preliminary data on non-bilateral aid transferred by governments to Ukraine of 40 countries. We used the FP-Growth algorithm to identify non-obvious connections and patterns between different types of support for Ukraine. Results We created an associative rules model to detect non-obvious patterns and relationships between the different types of bilateral commitments of the countries, that support Ukraine. Novelty Preliminary estimates were obtained between various types of international support for Ukraine in the war against Russia covering February 24 to November 20, 2022, the number of Ukrainian refugees accepted by the respective countries, and the sale of arms to Russia by some of them after the ban.

Reducing complexity in cyber security architecture: A practical model for security classifications

Building and running cyber security in both worlds, modern cloud security in combination with legacy on premises, introduces extra complexity. Some of the well-known security patterns and models are not applicable in cloud systems, while modern security models like zero trust (ZT) barely fit into legacy systems. Security technologies and tools are the subject of constant enhancements and adaptions to their environment. They can make security decisions on a very fine-grained basis. The corresponding rule sets and policies are becoming more and more decentralised, detailed and complex. Introducing modern security models such as ZT or micro-perimeter enforces the effect. The overall situation makes it hard for the responsible person to control the cyber security situation and the staff operating cyber security systems and technologies. Both are overwhelmed by the mass of fine-grained, fragmented and distributed security workloads. This paper introduces a practical model for security classifications in cyber security environments. The main goal of the model is to reduce complexity and keep cyber environments manageable. The model delivers not only a cyber risk classification regarding a single business application but works as an integrated view over risks for complete cyber environments.