Security Domain Research Articles

Analysing Revitalised Security Industry: The Tech-Powered Transformation for Small States

This scholarly endeavour delves into the extensive metamorphosis undergone by the security industry, a consequence of profound technological advancements. In particular, the study discerns the strategic utilisation of the tech-powered revitalised security industry by certain small states as a means to transcend the confines of defensive nihilism. An identified gap in current scholarship lies in the limited exploration of the transformative dynamics associated with technological innovation-driven security industries within the context of small states. Consequently, this paper endeavours to furnish a comprehensive analysis of the revitalised security industry and its multifaceted implications on the domains of security, politics, and economics. The research employs a nuanced approach, with a primary emphasis on case studies pertaining to Israel and Singapore, both exemplifying small states that have strategically harnessed high-tech capabilities to defy conventional perceptions of their inherent limitations. The central objective is to scrutinise the methodologies adopted by these small states, offering insights into their concerted efforts to bolster technological prowess. This divergent trajectory challenges prevailing realist assumptions regarding the inherent constraints imposed upon small states. Methodologically, the study employs a diverse array of analytical tools, including case study techniques, policy discourse investigation, and cross-case comparative analysis. This eclectic methodological approach aims to discern and unravel the intricate interplay between technology’s transformative influence on the security industry and its subsequent ramifications for small states.

Levenberg–Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning

Watermarking is one of the crucial techniques in the domain of information security, preventing the exploitation of 3D Mesh models in the era of Internet. In 3D Mesh watermark embedding, moderately perturbing the vertices is commonly required to retain them in certain pre-arranged relationship with their neighboring vertices. This paper proposes a novel watermarking authentication method, called Nearest Centroid Discrete Gaussian and Levenberg–Marquardt (NCDG–LV), for distortion detection and recovery using salient point detection. In this method, the salient points are selected using the Nearest Centroid and Discrete Gaussian Geometric (NC–DGG) salient point detection model. Map segmentation is applied to the 3D Mesh model to segment into distinct sub regions according to the selected salient points. Finally, the watermark is embedded by employing the Multi-function Barycenter into each spatially selected and segmented region. In the extraction process, the embedded 3D Mesh image is extracted from each re-segmented region by means of Levenberg–Marquardt Deep Neural Network Watermark Extraction. In the authentication stage, watermark bits are extracted by analyzing the geometry via Levenberg–Marquardt back-propagation. Based on a performance evaluation, the proposed method exhibits high imperceptibility and tolerance against attacks, such as smoothing, cropping, translation, and rotation. The experimental results further demonstrate that the proposed method is superior in terms of salient point detection time, distortion rate, true positive rate, peak signal to noise ratio, bit error rate, and root mean square error compared to the state-of-the-art methods.

A Game-Theoretical Self-Adaptation Framework for Securing Software-Intensive Systems

Security attacks present unique challenges to the design of self-adaptation mechanism for software-intensive systems due to the adversarial nature of the environment. Game-theoretical approaches have been explored in security to model malicious behaviors and design reliable defense for the system in a mathematically grounded manner. However, modeling the system as a single player, as done in prior works, is insufficient for the system under partial compromise and for the design of fine-grained defensive policies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To address such issues, we propose a new self-adaptation framework incorporating Bayesian game theory and model the defender (i.e., the system) at the granularity of components. Under security attacks, the architecture model of the system is automatically translated, by the proposed translation process with designed algorithms, into a multi-player Bayesian game. This representation allows each component to be modelled as an independent player, while security attacks are encoded as variant types for the components. By solving for pure equilibrium (i.e., adaptation response), the system’s optimal defensive strategy is dynamically computed, enhancing system resilience against security attacks by maximizing system utility. We validate the effectiveness of our framework through two sets of experiments using generic benchmark tasks tailored for the security domain. Additionally, we exemplify the practical application of our approach through a real-world implementation in the Secure Water Treatment System to demonstrates the applicability and potency in mitigating security risks.

Vietnam's Response to the US Indo-Pacific Strategy in the Context of a Rising China

As a less-powerful state threatened by a rising China in both security and economic domains irrespective of increasing economic cooperation and sharing a border and political affinity with China, Vietnam is expected to warmly welcome the United States (US) -led Indo-Pacific Strategy (IPS) The country, nonetheless, has remained low-key in responding to the IPS. The article argues that Hanoi is truly interested in a regional rules-based order promoted in the IPS, but it has proposed a hedging response to this strategy through cautious statements and limited engagement in into this strategy. Vietnam's hedging response to the IPS is framed by its post-Doi Moi foreign policy of independence, self-reliance, multilateralisation, and diversification. The year 2023 marks a milestone in bilateral relations between the US and Vietnam as both states upgraded their diplomatic relations to comprehensive strategic partnership which is the highest level in Vietnamese diplomatic ranking. This may become a favourable catalyst for further cooperation between Washington and Hanoi under the IPS within the context of a rising China.

Risk Analysis Research on SMS Verification Code and Biometric Recognition Technology

This paper delves into the ongoing transformation in the domain of digital identity security. It specifically focuses on the paradigm shift from traditional methods of authentication, such as passwords and text message verification codes, towards the use of biometric recognition technology. By scrutinizing the inherent weaknesses linked with the utilization of text message verification codes, the paper underlines the demand for more trustworthy and secure solutions for authentication. Following this, biometric authentication is thoroughly examined, with a highlight on its unique features, the convenience it provides to users, and its capacity for ongoing verification. The discussion extends to practical applications of biometrics in a variety of sectors, including healthcare and high-security workplaces, as well as in situations where employing text message verification is unsuitable. A focal point of the discussion is multi-modal biometric authentication. While acknowledging the potential hurdles and future possibilities tied to the adoption of biometric technology, the paper offers an objective perspective. The overall intent of this research is to furnish insights that will enable informed decision-making regarding the deployment and improvement of digital identity verification methods across a wide range of industries.

Recurrent Exposure Generation for Low-Light Face Detection

This Project introduces an innovative approach to low-light face detection, Exploiting Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and the Zero-Reference Deep Curve Estimation (Zero-DCE) algorithm. The primary goal is to improve the accuracy and reliability of face detection in challenging low-light conditions. Our method integrates LSTM and CNN networks with real-time exposure control, enabling adaptation to dynamic lighting conditions by capturing multiple frames iteratively with varying exposure levels. The incorporation of Zero-DCE facilitates the enhancement of exposure settings, resulting in improved face visibility and noise reduction. Experimental evaluations validate the efficiency of our approach, demonstrating significant advancements in low-light face detection accuracy compared to traditional methods. This project offers a practical adaptable solution with wide-ranging implications for real- world applications, including surveillance, security, and various other domains. Keywords – Low-light face detection, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), Zero- Reference Deep Curve Estimation (Zero-DCE), recurrent exposure generation, real-time exposure control, dynamic lighting conditions, noise reduction, surveillance.

An assessment of critical success factors in information security implementation in organizations in Ghana

Purpose This paper aims to identify the critical success factors in improving information security in Ghanaian firms. Design/methodology/approach Through an exploratory study of both public and private Ghanaian organizations. The study relied on a research model based on the technology–organization–environment (TOE) framework and a survey instrument to collect data from 525 employees. The data was analyzed using partial least squares-structural equation modeling (PLS-SEM). Findings The findings confirm the role of the technological, organizational and environmental contexts as significant determinants in the implementation of information security in Ghanaian organizations. Results from PLS-SEM analysis demonstrated a positive correlation between the technology component of information security initiative, organization’s internal efforts toward its acceptance and a successful implementation of information security in Ghanaian firms. Top management support and fund allocation among others will result in positive information security initiatives and positive attitudes toward securing the organization’s information assets. Research limitations/implications The authors discussed the implications of the authors’ findings for research, practice and policy. Social implications The results of this study will be useful for both governmental and non-governmental organizations in terms of best practices for increasing information security. Results from this study will aid organizations in developing countries to better understand their information security needs and identify the necessary procedures to address them. Originality/value This study contributes to filling the knowledge gap in organizational information security research and the TOE framework. Despite the TOE framework being one of the most influential theories in contemporary research of information system domains in an organizational context, there is not enough research linking the domains of information security and the TOE model.

An approach based on hexagram model for quantifying security risks with Performance Key Indicators (PKI)

As organizations grapple with an ever-evolving threat landscape, the need for effective security risk quantification methodologies becomes paramount. This research paper introduces and explores a novel approach to security risk quantification through the application of a hexagram model. Drawing inspiration from the I Ching, an ancient Chinese divination text, this hexagram model encompasses six key elements: Threat Landscape, Vulnerability Analysis, Asset Criticality, Control Effectiveness, Incident Response Capability, and Business Impact. By dividing these elements into two trigrams, a comprehensive view of external and internal factors influencing security risk emerges. The literature review examines existing security risk quantification methods, highlighting their strengths and limitations. The hexagram model's uniqueness lies in its holistic representation, integrating technical and organizational facets. The paper details the methodology, providing a clear framework for application. A practical case study demonstrates the model's implementation, showcasing its efficacy in real-world security assessments. Results and analysis reveal valuable insights into the security risk landscape, with comparisons to traditional quantification methods illustrating the hexagram model's added depth. The discussion interprets findings in the context of the security domain, addressing implications and areas for improvement. The paper concludes by summarizing key contributions, emphasizing the significance of the hexagram model in providing a nuanced understanding of security risk. Recommendations for future research underscore the potential for further refinement and broader adoption of this innovative approach.

Ontology-based security modeling in ArchiMate

Enterprise Risk Management involves the process of identification, evaluation, treatment, and communication regarding risks throughout the enterprise. To support the tasks associated with this process, several frameworks and modeling languages have been proposed, such as the Risk and Security Overlay (RSO) of ArchiMate. An ontological investigation of this artifact would reveal its adequacy, capabilities, and limitations w.r.t. the domain of risk and security. Based on that, a language redesign can be proposed as a refinement. Such analysis and redesign have been executed for the risk elements of the RSO grounded in the Common Ontology of Value and Risk. The next step along this line of research is to address the following research problems: What would be the outcome of an ontological analysis of security-related elements of the RSO? That is, can we identify other semantic deficiencies in the RSO through an ontological analysis? Once such an analysis is provided, can we redesign the security elements of the RSO accordingly, in order to produce an improved artifact? Here, with the aid of the Reference Ontology for Security Engineering (ROSE) and the ontological theory of prevention behind it, we address the remaining gap by proceeding with an ontological analysis of the security-related constructs of the RSO. The outcome of this assessment is an ontology-based redesign of the ArchiMate language regarding security modeling. In a nutshell, we report the following contributions: (1) an ontological analysis of the RSO that identifies six limitations concerning security modeling; (2) because of the key role of the notion of prevention in security modeling, the introduction of the ontological theory of prevention in ArchiMate; (3) a well-founded redesign of security elements of ArchiMate; and (4) ontology-based security modeling patterns that are logical consequences of our proposal of redesign due to its underlying ontology of security. As a form of evaluation, we show that our proposal can describe risk treatment options, according to ISO 31000. Finally, besides presenting multiple examples, we proceed with a real-world illustrative application taken from the cybersecurity domain.

Exploring visual techniques for indoor intrusion detection using detectron2 and Faster RCNN

As societal evolution marches forward, there's an escalating emphasis on indoor security concerns. Within the safety realm, indoor intrusion detection emerges as a pivotal challenge, given its direct implications on safeguarding human lives and assets. Leveraging visual methods for indoor intrusion detection holds promising potential, particularly due to its straightforward deployment advantages. This study zeroes in on surveillance video footage, a prevalent medium in the security domain, as its experimental muse. Post an initial preprocessing phase utilizing a video difference map, the research introduces a pedestrian detection algorithm hinged on the synergy of detectron2 and Faster R-CNN. Insights gleaned reveal that this combined algorithm, when augmented with the video difference map preprocessing, exhibits commendable accuracy, robustness, and real-time efficacy on surveillance footage, especially in scenarios bereft of significant target occlusion. Moreover, this algorithm showcases an adeptness at discerning diminutively sized targets, demonstrating resilience against varying light magnitudes and maintaining impeccable accuracy amidst intricate lighting conditions. By harnessing this methodology, the enhancement of indoor environment safety monitoring becomes feasible, thereby bolstering the provision of dependable protection for individuals.

Adaptive dynamic safety domain for different sailing conditions

The problem of navigation safety is considered, in particular, the problem of developing an algorithm for an adaptive dynamic safety domain for various navigation conditions is investigated. The risk assessment of a ship approach situation is based on the safety domain as the minimum area around the ship into which an oncoming ship should not enter. Navigation conditions are considered in accordance with the rules of COLREG‑72 for navigation of vessels with limited visibility and being in sight of each other. To solve this problem, the navigation areas are classified. For further analysis, a table for the conditions and areas of navigation to identify the shape of the domains is created. The hydro meteorological situation, which takes into account the navigation conditions, and as a consequence, the need to increase the size of the safety domain, is considered. Changes in the parameters and shape of ship safety domains for different navigation conditions have been identified. For each district and navigation conditions, an original configuration of the security domain is proposed. The dynamic changes of the domain during movement in traffic separation systems and channels are separately considered. Using an example, a situation when the bow of the domain crosses the dividing line, which can lead to the appearance of false dangerous targets, is simulated. To avoid such situations, a dynamic domain with a changing shape in the form of an ellipse for swimming in a traffic separation system and channels, taking into account sailing conditions with limited visibility and in sight of each other is proposed. For the calculation, an algorithm to determine the shape of the domain under different visibility conditions and navigation areas has been compiled. Calculations for the case when the vessel enters a turn in traffic separation systems and narrow spaces have been performed. The deformation of the domain during rotation is shown.

‘Our region is now a strategic theatre’: New Zealand’s balancing response to China

Drawing upon empirical evidence this article explains that, as the US-China Great Power Competition intensifies, New Zealand – a small state in the South Pacific – has shifted its view of Beijing from a potential security partner to a revisionist actor challenging the regional status quo and the international rules-based order that Wellington repeatedly declares is critical to its long-term security. China’s rise and assertive behaviour is placing structural pressure on New Zealand to react and altered its threat assessment of Beijing. This article reveals this by detailing New Zealand’s balancing strategy in the security domain vis-a-vis China from 2010. This includes an initial phase of pre-balancing through deepened ties with Washington, then transition to active balancing via acquisition of advanced aircraft, space co-operation, joint naval manoeuvres, and initiatives in the Pacific. A suite of new strategy documents released by the New Zealand government in August 2023 - and statements made by the new conservative National Party-led coalition government in 2024 - signals that balancing is set to continue and portends the rejuvenation of the NZ Defence Force (which has been neglected and under-resourced in recent times). New Zealand’s potential membership of Pillar 2 of the AUKUS security pact is considered, and how Wellington is presently managing its relationships with the US and China within a broader asymmetric hedging strategy. The article concludes that if China’s behaviour demonstrably changes, so too may Wellington’s assessment of the China threat and, therefore, Wellington’s response. It also outlines area where additional research could prove fruitful.

Structuring and organizing database security domain from big data perspective using meta-modeling approach

Database security is an area focused on safeguarding databases against harmful access. It involves ensuring data accuracy, blocking unauthorized entry, and preventing harmful code within the database. Although various security models and methods exist, they often don't comprehensively cover all aspects of database security. This leads to a diverse and unclear understanding of database security among experts. This study proposes a unified framework, the Database Security Meta-model (DBSM), which acts as a standard language in this field. The DBSM, comprising twelve main elements, is thoroughly vetted to align with security needs and offers guidelines for practitioners to create specific security solutions.

The Role of Computer Teachers in the Kasbah of Amman Schools in Developing Digital Intelligence in Distance Learning among their Students from the Point of View of their Principals

The present study aimed to investigate the role of computer teachers in Kasbah Amman schools in developing digital intelligence in distance learning among their students from the point of view of their principals. The descriptive survey methodology was adopted. The validity and study sample consisted of (120) principals, and the questionnaire that was developed reliability of the tool were assured. The results of the study showed that role of the computer teachers in developing digital intelligence in distance learning among their students from the point of view of their principals came to a large degree, and that the highest domain were the domain of digital security and safety development, followed by the domain of use and digital literacy, and there were no statistically significant differences attributed to the two variables (gender, and experience).

Financial Risk Assessment Model Based on Fuzzy Logic

In the rapidly evolving landscape of business operations, establishing a robust security domain prevention system in the Middle Office is of utmost importance. To achieve this, the integration of Choquet expectation-based methodologies offers a powerful approach. The Middle Office acts as a critical hub for various business processes, including risk management, compliance, and data protection. With the implementation of Choquet expectation theory, which encompasses the combination of multiple criteria and preferences, businesses can effectively assess and optimize their security domain prevention system. The establishment and optimization of a security domain prevention system based on Choquet’s expectations provide businesses with a comprehensive and tailored approach to protect their critical assets, maintain operational continuity, and safeguard sensitive data from emerging threats in the Middle Office environment. This paper constructed a Fuzzy Optimization Membership Estimation (FOME) for the computation of the feature vector. The proposed FOME model uses the Flemingo Optimization model for the evaluation of the feature vector in the business middle office. The FOME model effectively computes the Choquet expectation features for the analysis of the risk management of the feature vector in the middle office. Through the membership estimation with the FOME model, the model significantly computes the different attacks in the middle office. The analysis of the proposed FOME is evaluated for the conventional CICIDS dataset for the attack analysis. The simulation analysis stated that the proposed FOME model achieves a higher classification accuracy of 99.89% for attack detection.

White-box validation of quantitative product lines by statistical model checking and process mining

We propose a novel methodology to validate software product line (PL) models by integrating Statistical Model Checking (SMC) with Process Mining (PM). We consider the feature-oriented language QFLan from the PL engineering domain. QFLan allows to model PL equipped with rich cross-tree and quantitative constraints, as well as aspects of dynamic PLs such as the staged configurations. This richness allows us to easily obtain models with infinite state-space, calling for simulation-based analysis techniques, like SMC. For example, we use a running example with infinite state space. SMC is a family of analysis techniques based on the generation of samples of the dynamics of a system. SMC aims at estimating properties of a system like the probability of a given event (e.g., installing a feature), or the expected value of quantities in it (e.g., the average price of products from the studied family). Instead, PM is a family of data-driven techniques that uses logs collected on the execution of an information system to identify and reason about its underlying execution process. This often regards identifying and reasoning about process patterns, bottlenecks, and possibilities for improvement. In this paper, to the best of our knowledge, we propose, for the first time, the application of Process Mining (PM) techniques to the byproducts of Statistical Model Checking (SMC) simulations. This aims to enhance the utility of SMC analyses. Typically, if SMC gives unexpected results, the modeler has to discover whether these come from actual characteristics of the system, or from bugs in the model. This is done in a black-box manner, only based on the obtained numerical values. We improve on this by using PM to get a white-box perspective on the dynamics of the system observed by SMC. Roughly speaking, we feed the samples generated by SMC to PM tools, obtaining a compact graphical representation of the observed dynamics. This mined PM model is then transformed into a mined QFLan model, making it accessible to PL engineers. Using two well-known PL models, we show that our methodology is effective (helps in pinpointing issues in models, and in suggesting fixes), and that it scales to complex models. We also show that it is general, by applying it to the security domain.

Steps towards Satisficing Distributed Dynamic Team Trust

Defining and measuring trust in dynamic, multiagent teams is important in a range of contexts, particularly in defense and security domains. Team members should be trusted to work towards agreed goals and in accordance with shared values. In this paper, our concern is with the definition of goals and values such that it is possible to define ‘trust’ in a way that is interpretable, and hence usable, by both humans and robots. We argue that the outcome of team activity can be considered in terms of ‘goal’, 'individual/team values', and ‘legal principles’. We question whether alignment is possible at the level of 'individual/team values', or only at the 'goal' and ‘legal principles’ levels. We argue for a set of metrics to define trust in human-robot teams that are interpretable by human or robot team members, and consider an experiment that could demonstrate the notion of 'satisficing trust' over the course of a simulated mission.

The Ramifications of China’s Maritime Equipment Industry Policies for the EU

ABSTRACT This article utilizes strategic trade theory (STT) to assess China’s build-up of its maritime equipment (ME) industry. It does so by analyzing how maritime equipment has been addressed in Five-Year-Plans (FYPs) on the development of the maritime economy since 2013. Thus, it illustrates the importance of constructing integrated demonstration development zones and building-up technological capacities for the ME industry’s development. It also details the Chinese government’s objective to develop national champions in the ME industry, which corresponds with the broader context of China’s Innovation-Driven Development Strategy (IDDS) since 2016. Though trade data illustrates China has not yet taken over the EU’s current leadership position in the ME value chain, it does signal that dependence on the EU has been inversed for several categories of the ME industry. EU policymakers need to reflect on the possible ramifications of China’s approach for both the EU’s economic and security domains in deciding on if, how, and to what degree the EU and its member states should address China’s ME build-up.

An enhanced abnormal information expression spatiotemporal model for anomaly detection in multivariate time-series

The detection of anomalies in high-dimensional time-series has always played a crucial role in the domain of system security. Recently, with rapid advancements in transformer model and graph neural network (GNN) technologies, spatiotemporal modeling approaches for anomaly detection tasks have been greatly improved. However, most methods focus on optimizing upstream time-series prediction tasks by leveraging joint spatiotemporal features. Through experiments, we found that this modeling approach not only risks the loss of some original anomaly information during data preprocessing, but also focuses on optimizing the performance of the upstream prediction task and does not directly enhance the performance of the downstream detection task. We propose a spatiotemporal anomaly detection model that incorporates an improved attention mechanism in the process of temporal modeling. We adopt a heterogeneous graph contrastive learning approach in spatio modeling to compensate for the representation of anomalous behavioral information, thereby guiding the model through thorough training. Through validation on two widely used real-world datasets, we demonstrate that our model outperforms baseline methods. We also explore the impact of multivariate time-series prediction tasks on the detection task, and visualize the reasons behind the benefits gained by our model.

Evaluation of Malware Classification Models for Heterogeneous Data.

Machine learning (ML) has found widespread application in various domains. Additionally, ML-based techniques have been employed to address security issues in technology, with numerous studies showcasing their potential and effectiveness in tackling security problems. Over the years, ML methods for identifying malicious software have been developed across various security domains. However, recent research has highlighted the susceptibility of ML models to small input perturbations, known as adversarial examples, which can significantly alter model predictions. While prior studies on adversarial examples primarily focused on ML models for image processing, they have progressively extended to other applications, including security. Interestingly, adversarial attacks have proven to be particularly effective in the realm of malware classification. This study aims to explore the transparency of malware classification and develop an explanation method for malware classifiers. The challenge at hand is more complex than those associated with explainable AI for homogeneous data due to the intricate data structure of malware compared to traditional image datasets. The research revealed that existing explanations fall short in interpreting heterogeneous data. Our employed methods demonstrated that current malware detectors, despite high classification accuracy, may provide a misleading sense of security and measuring classification accuracy is insufficient for validating detectors.