Security Posture Research Articles

A Study on a Scenario-Based Security Incident Prediction System for Cybersecurity

In the 4th industrial era, the proliferation of interconnected smart devices and advancements in AI, particularly big data and machine learning, have integrated various industrial domains into cyberspace. This convergence brings novel security threats, making it essential to prevent known incidents and anticipate potential breaches. This study develops a scenario-based evaluation system to predict and evaluate possible security accidents using the MITRE ATT&CK framework. It analyzes various security incidents, leveraging attack strategies and techniques to create detailed security scenarios and profiling services. Key contributions include integrating security logs, quantifying incident likelihood, and establishing proactive threat management measures. The study also proposes automated security audits and legacy system integration to enhance security posture. Experimental results show the system’s efficacy in detecting and preventing threats, providing actionable insights and a structured approach to threat analysis and response. This research lays the foundation for advanced security prediction systems, ensuring robust defense mechanisms against emerging cyber threats.

Cybersecurity in the Petroleum Industry: A Comprehensive Review of Threats and Mitigation Strategies

The reliance of the petroleum industry on digital technologies is on the increase. This increase has introduced significant cyber security risks, threatening operations, safety, and the environment. This review examines the current state of cyber security in the petroleum industry, highlighting the range of threats, including phishing, ransom ware, OT-specific risks, like attacks on SCADA and ICS systems, and industrial control system attacks. It also assesses the effectiveness of countermeasures, such as risk assessment, incident response planning, employee training, and advanced security measures like AI and machine learning. The study emphasizes the importance of compliance with industry-specific standards and certification, continuous monitoring, and adaptation to emerging technologies and evolving threats. Some of the cyber security standards and protocols include NIST, ISO/IEC 27001 and CIS. Their duties include provide guidelines to assist organizations in conducting appropriate and effective management and mitigation of risks, establishing a sound information security management system that focuses on safeguarding sensitive data and outline a prioritized method by which one could guard against common cyber threats, putting an emphasis on key areas of security, access control, and response to incidents respectively. This review aims to inform and guide the petroleum industry in strengthening its cyber security posture and ensuring the resilience of its operations as well as this review serves as a guide for leadership on resilience and proactive threat management, addressing critical vulnerabilities.

Enhancing Insider Threat Detection through Integrated Behavioral, Signature and Anomaly based Detection Methods

Insider threats present substantial risks to organizational security, as malicious actors exploit their authorized access to systems, networks, or data to perpetrate harmful activities. These threats encompass various forms, including data theft, sabotage, fraud, or espionage, leading to significant financial losses, reputational damage, or regulatory penalties. Traditional approaches to insider threat detection, such as anomaly-based, signature-based, and behavioral analysis methods, have inherent limitations, including high false positives, reliance on known patterns, and lack of contextual understanding. These approaches often fail to classify insider threats accurately, potentially leading to innocent insiders being mislabeled as malicious. In this project, a unified insider threat detection system is proposed, integrating anomaly-based, signature- based, and behavioral analysis methods using Support Vector Machines (SVMs). By combining these methods and leveraging the strengths of SVMs, the aim is to address the limitations of individual approaches and enhance detection accuracy. Weighted voting is employed to fuse the output of each detection method, providing a comprehensive likelihood estimate of insider threats. This integrated approach enables organizations to better identify and mitigate insider threats, safeguarding sensitive assets and maintaining a robust security posture. Keywords— Insider threat, Insider threat detection, Signature- based detection, Anomaly-based detection, Behavior analysis, False positives, Detection accuracy, Weighted Voting Mechanism.

Cybersecurity strategies for non-profit organizations

The paper explores effective cybersecurity strategies tailored for non-profit organizations, addressing their unique challenges of limited resources and sensitive data handling. It examines the current threat landscape, highlighting common vulnerabilities such as phishing attacks, ransomware, and data breaches. The research presents key cybersecurity strategies, including risk assessment and management, employee training and awareness, implementation of technical controls, data protection and privacy measures, and third-party risk management. Cost-effective solutions are emphasized, focusing on open-source security tools, cloud-based services, and collaborative information-sharing networks. The study concludes that by adopting a holistic approach encompassing people, processes, and technology, non-profits can significantly enhance their security posture and resilience against cyber threats, ensuring the continuity of their critical missions while operating within resource constraints.

Early Detection of Hard-Coded Secrets in Software Development: A Multi-Method Approach Integrating Static Analysis, Entropy-Based Detection, and Machine Learning

The inadvertent inclusion of hard-coded secrets—such as API keys, passwords, and tokens—within source code poses significant security risks, potentially leading to unauthorized access and data breaches. Recent studies have highlighted the prevalence of this issue; for instance, GitGuardian's 2023 report revealed a 67% increase in detected hard-coded secrets compared to the previous year, with 10 million new secrets discovered in public GitHub commits in 2022. To address this escalating concern, our research proposes a comprehensive, multi-method approach that integrates static code analysis, entropy-based detection, and machine learning techniques to enhance the early identification of hard-coded secrets during the software development lifecycle. By combining these methodologies, the proposed approach improves detection accuracy, reduces false positives, and provides developers with actionable insights to mitigate security vulnerabilities proactively. This integrated strategy not only strengthens the security posture of software applications but also fosters a culture of secure coding practices among development teams. Keywords Shift-Left Security, Secrets Detection, Static Analysis, Entropy-Based Detection, Machine Learning, Hard-Coded Secrets, Secure Development Lifecycle

CSPM for Healthcare: Securing Electronic Health Records (EHR) and Ensuring HIPAA Compliance

Cloud Security Posture Management (CSPM) has emerged as a critical solution for healthcare organizations navigating the complexities of digital transformation and cloud adoption. This comprehensive article examines how CSPM tools help healthcare providers secure Electronic Health Records (EHR) while maintaining HIPAA compliance in cloud environments. The article explores key challenges in maintaining data privacy and security requirements, evaluates the role of CSPM in automated security assessment and compliance monitoring, and presents real-world case studies demonstrating successful implementations. Additionally, it investigates future directions in CSPM development, including artificial intelligence integration and enhanced data protection features, providing insights into the evolving landscape of healthcare security management.

Adoption of Advanced Cybersecurity Tools by Organizations: Motivations, Barriers, and Leader Responses

This study explores the adoption of advanced cybersecurity tools in organizations, focusing on the motivations, barriers, and leadership responses that shape adoption decisions. As cyber threats grow in sophistication, organizations must adopt increasingly complex tools to defend against attacks. However, despite the availability of cutting-edge technologies, adoption remains inconsistent. Through semi-structured interviews with 19 executive decision-makers and cybersecurity experts from high-threat industries, the study identifies key drivers for tool adoption, including an escalating threat environment and the need to enhance defense capabilities. It also highlights significant barriers, such as challenges of technical integration, organization readiness, and vendor management. Leadership plays a pivotal role in overcoming these obstacles by aligning organizational goals, securing resources, and fostering a culture of collaboration. The findings contribute to the literature on innovation adoption and cybersecurity management, offering insights into how organizations can better integrate advanced tools to enhance their security posture.

Best Practices for Database Security in the Age of AI

Database security has seen a dramatic change as a result of artificial intelligence's incorporation into contemporary technology, bringing with it previously unheard-of opportunities as well as obstacles. This thorough article looks at how businesses can use AI capabilities to safeguard sensitive data assets while fortifying their database security postures. With an emphasis on financial institutions and healthcare organizations, it looks at the changing threat landscape, crucial security measures, and practical implementation tactics across a range of sectors. The article further demonstrates the efficacy of threat detection systems, sophisticated encryption techniques, AI-powered security solutions, and thorough audit frameworks. It illustrates the vital significance of putting in place multi-layered security techniques that combine conventional safeguards with AI-enhanced protective measures through in-depth case studies and empirical analysis.

Securing the Future: Cybersecurity Challenges and Solutions in Digital Oilfields

Digital oilfield is a concept that applies advanced technology to automate workflows in the oil and gas industry for the sole purpose of maximizing production, reducing costs, and minimizing the overall risks associated with operations. However, despite the numerous advantages, careful planning and mitigation strategies put in place, there has been a spike in the rate of cyber-attacks carried out on critical infrastructure across various industries. Cyber threats like malware, ransomware, code injection attacks and phishing attacks pose significant risks to the smooth functioning of these digital infrastructures. Its sensitivities, including outdated legacy systems, Supervisory Control and Data Acquisition (SCADA) systems, and numerous IoT devices, including human error also play considerable roles in cybersecurity breaches, highlighting the need for robust security protocols and strategies. However, with the advent of safer AI technologies, enhanced employee training on cybersecurity, cyber incidents as reported in the case studies, could be mitigated. This research examines critical cyber-attacks in the oil and gas industry to identify vulnerabilities and develop robust preventive strategies. The findings emphasize the importance of a multi-layered security approach, including network segmentation, end-to-end data encryption, and systematic software patch management. Organizations must implement comprehensive incident response plans and conduct regular security audits to maintain operational resilience. The study highlights that effective cybersecurity in the oil and gas sector requires both regulatory compliance and strategic asset prioritization. By identifying and classifying critical infrastructure, companies can allocate resources more effectively and strengthen their security posture where it matters most. The research demonstrates that successful incident management hinges on well-defined response and recovery protocols. Emerging technologies play a pivotal role in advancing cybersecurity defenses. Artificial intelligence enables predictive threat detection, while blockchain technology enhances data integrity and traceability. Cloud security solutions and machine learning algorithms provide scalable, adaptive protection against evolving threats. Through evaluation of case study of cyber incidents across two distinct sectors, it becomes imperative that cybersecurity is a topic that cuts across various sectors and demands industry collaboration and stringent regulatory compliance to avoid future breaches.

Integrated Transcriptome and sRNAome Analysis Reveals the Molecular Mechanisms of Piriformospora indica-Mediated Resistance to Fusarium Wilt in Banana.

Bananas (Musa spp.) are among the most important fruit and staple food crops globally, holding a significant strategic position in food security in tropical and subtropical regions. However, the industry is grappling with a significant threat from Fusarium wilt, a disease incited by Fusarium oxysporum f. sp. cubense (Foc). In this study, we explored the potential of Piriformospora indica (Pi), a mycorrhizal fungus renowned for bolstering plant resilience and nutrient assimilation, to fortify bananas against this devastating disease. Through a meticulous comparative analysis of mRNA and miRNA expression in control, Foc-inoculated, Pi-colonized, and Pi-colonized followed by Foc-inoculated plants via transcriptome and sRNAome, we uncovered a significant enrichment of differentially expressed genes (DEGs) and DE miRNAs in pathways associated with plant growth and development, glutathione metabolism, and stress response. Our findings suggest that P. indica plays a pivotal role in bolstering banana resistance to Foc. We propose that P. indica modulates the expression of key genes, such as glutathione S-transferase (GST), and transcription factors (TFs), including TCP, through miRNAs, thus augmenting the plant's defensive capabilities. This study offers novel perspectives on harnessing P. indica for the management of banana wilt disease.

Japan's Security Restoration: Evolution, Challenges, and Future Directions

ObjectivesThis study examines Japan's security policy evolution from post-World War II pacifism to its current assertive stance, analyzing historical context, key drivers, and challenges. It focuses on shifts under Prime Minister Shinzo Abe, exploring implications for regional stability and international relations, including the U.S.-Japan alliance and emerging security threats.MethodsThe research employs a qualitative approach, combining comprehensive literature review with multiple theoretical frameworks (neorealism, constructivism, securitization theory, alliance theory). It incorporates historical and comparative analyses to contextualize Japan's security posture within regional dynamics.ResultsJapan's security policy has transformed significantly, driven by China's military modernization, North Korea's nuclear program, changing regional dynamics, and evolving U.S.-Japan alliance expectations. Key reforms include constitutional reinterpretation allowing limited collective self-defense, establishing the National Security Council, increased defense spending, and enhanced multi-domain military capabilities. Challenges persist in balancing security needs with constitutional constraints and managing regional relationships.ConclusionsJapan's security restoration marks a profound shift, balancing proactive defense with pacifist principles. This evolution impacts regional stability and the U.S.-Japan alliance, positioning Japan as a key player in maintaining peace while raising some neighbors' concerns. Japan's path forward requires a delicate equilibrium between enhancing military capabilities and prioritizing diplomacy and economic cooperation. Success depends on navigating historical legacies, fostering domestic consensus, and adeptly maneuvering through complex international imperatives in an increasingly challenging security environment.

Enhancing Cloud Security: Strategies and Technologies for Protecting Data in Cloud Environments

With the rapid adoption of cloud computing, ensuring the security of data has become a paramount concern for organizations of all sizes. This paper explores various strategies and technologies for protecting data in cloud environments. Encryption emerges as a fundamental technique for safeguarding data both at rest and in transit, with block ciphers and stream ciphers offering robust encryption methods. Additionally, hash functions play a critical role in verifying data integrity and ensuring secure data storage. We delve into the implementation of these techniques in cloud environments, highlighting best practices for encryption, key management, and data integrity verification. Moreover, we address common cloud vulnerabilities, such as lack of appropriate governance, isolation failure, and malicious attacks, and discuss mitigation strategies to strengthen cloud security posture. By leveraging encryption, block ciphers, stream ciphers, and hash functions, organizations can enhance data protection in the cloud and mitigate the risks associated with unauthorized access, data breaches, and tampering. This paper serves as a comprehensive guide for organizations seeking to enhance their cloud security and maintain trust in cloud-based services.

The Rise of AI-powered Cybercrime: a Data-driven Analysis of Emerging Threats

The integration of artificial intelligence (AI) in cybercrime represents a transformative shift in the global threat landscape, introducing unprecedented challenges to cybersecurity frameworks. This comprehensive article examines the dramatic 238% surge in AI-powered cyber attacks during 2023, resulting in global damages exceeding $8.5 billion. Through analysis of 100,000 malware samples, 50,000 documented incidents, and 2,500 attack patterns across Fortune 500 companies, this study quantifies the impact of AI integration in cyber attacks. Key findings reveal that AI-enhanced attacks achieve a 67% higher success rate while reducing operational complexity by 72%. The article demonstrates concerning trends across four critical domains: automated attacks, AI-enhanced phishing (showing an evolution from 2.9% to 8.7% success rate), malware evolution, and deepfake-based fraud. The article also presents actionable mitigation strategies, including AI-based security solutions that demonstrate a 97.8% improvement in response efficiency, reducing incident response times from 6 hours to 8 minutes. This article provides a framework for organizations to strengthen their security posture against emerging AI-powered threats through systematic implementation of advanced defense mechanisms and strategic investment in AI-enhanced security solutions.

Zero Trust VPN (ZT-VPN): A Systematic Literature Review and Cybersecurity Framework for Hybrid and Remote Work

Modern organizations have migrated from localized physical offices to work-from-home environments. This surge in remote work culture has exponentially increased the demand for and usage of Virtual Private Networks (VPNs), which permit remote employees to access corporate offices effectively. However, the technology raises concerns, including security threats, latency, throughput, and scalability, among others. These newer-generation threats are more complex and frequent, which makes the legacy approach to security ineffective. This research paper gives an overview of contemporary technologies used across enterprises, including the VPNs, Zero Trust Network Access (ZTNA), proxy servers, Secure Shell (SSH) tunnels, the software-defined wide area network (SD-WAN), and Secure Access Service Edge (SASE). This paper also presents a comprehensive cybersecurity framework named Zero Trust VPN (ZT-VPN), which is a VPN solution based on Zero Trust principles. The proposed framework aims to enhance IT security and privacy for modern enterprises in remote work environments and address concerns of latency, throughput, scalability, and security. Finally, this paper demonstrates the effectiveness of the proposed framework in various enterprise scenarios, highlighting its ability to prevent data leaks, manage access permissions, and provide seamless security transitions. The findings underscore the importance of adopting ZT-VPN to fortify cybersecurity frameworks, offering an effective protection tool against contemporary cyber threats. This research serves as a valuable reference for organizations aiming to enhance their security posture in an increasingly hostile threat landscape.

Variance‐driven security optimisation in industrial IoT sensors

AbstractThe Industrial Internet of Things (IIoT) has transformed industrial operations with real‐time monitoring and control, enhancing efficiency and productivity. However, this connectivity brings significant security challenges. This study addresses these challenges by identifying abnormal sensor data patterns using machine learning‐based anomaly detection models. The proposed framework employs advanced algorithms to strengthen industrial defences against cyber threats and disruptions. Focusing on temperature anomalies, a critical yet often overlooked aspect of industrial security, this research fills a gap in the literature by evaluating machine learning models for this purpose. A novel variance‐based model for temperature anomaly detection is introduced, demonstrating high efficacy with accuracy scores of 0.92 and 0.82 on the NAB and AnoML‐IOT datasets, respectively. Additionally, the model achieved F1 scores of 0.96 and 0.89 on these datasets, underscoring its effectiveness in enhancing IIoT security and optimising cybersecurity for industrial processes. This research not only identifies security vulnerabilities but also presents concrete solutions to improve the security posture of IIoT systems.

The Effects of Personality Traits, Environmental Attitudes, and Demographic Factors on Green Party Support in Canada

Abstract By appealing to public concern over environmental issues, Green parties have emerged to gain secure positions in several party systems. However, in Canada, we know very little about why people support the Green Party. This research note draws upon the Canadian Election Study (CES) to explore the ways in which demographic factors, personality traits and individual environmentalism impact vote choice. Theorizing Green Party support as a form of pro-environmental behaviour, we build a model that tests the impact of demographic factors and personality traits as mediated through environmental attitudes. It finds that, while pro-environmental policy attitudes are the strongest predictor of Green Party support, several demographic factors and personality traits—specifically conscientiousness, openness to experience, agreeableness and extraversion—have an effect.

Transforming Cybersecurity Infrastructure : A Multi-dimensional Approach to Risk, Culture, and Technological Integration

The rapid acceleration of digital transformation has exposed critical limitations in traditional cybersecurity approaches, particularly in their reactive nature and disconnection from broader organizational strategies. This article critically examines current cybersecurity practices in IT infrastructure management, identifying significant gaps in the integration of security measures with business objectives, organizational culture, and emerging technologies. Through a mixed-method analysis of industry practices and empirical data from multiple case studies, The article proposes a comprehensive framework that transcends conventional security paradigms. The article introduces a proactive, risk-based approach that integrates cultural transformation, emerging technologies, and resilience building while fostering strategic partnerships across stakeholder groups. Initial implementation across various organizational contexts demonstrates significant improvements in security posture, incident response times, and overall business alignment. The findings contribute to both theoretical understanding and practical application of integrated cybersecurity management, offering valuable insights for practitioners and researchers in the field of IT security and organizational resilience. This article addresses a critical gap in current literature by providing a holistic approach that aligns cybersecurity initiatives with organizational transformation while considering human factors and technological evolution.

Land use change and its impact on ecosystem service value in Xinjiang

Abstract Xinjiang is an important hub connecting China and Eurasia and has an important strategic position in economy, energy, and ecological security. However, due to the fragile ecological environment in Xinjiang, there are substantial challenges in coordinating the relationship between social and economic development and ecological environment construction. To elucidate the relationship between land use changes induced by human activities and the valuation of ecosystem services, this study utilized the value equivalent method to evaluate the ecosystem service value in Xinjiang from 2000 to 2020 while analyzing its spatiotemporal response characteristics related to land use. The results showed that from 2000 to 2020, the value of ecosystem services in Xinjiang showed an upward trend, reaching 182.24 yuan/hm2 in 2020, and the total value of the whole region was 302.584 billion yuan. The value of ecological services in Xinjiang is mainly low-grade (about 50%), and high-grade only accounts for 1.03%~2.19%. There was a significant increase in land use in forested areas and water bodies, while the conversion trend from effective grassland to other land types was relatively limited. Specifically, 4.47% of bare ground and 3.76% of meadows transformed water bodies. Additionally, 2.69% of grassland experienced conversion into woodland. The land use intensity was generally low (more than 85% of the low-grade grades), and 83.71% of the land use intensity increased during the study period. The water area was the main controlling factor of the service value of the state system, with a contribution rate of 78.83%, followed by grassland (11.29%) and woodlands (9.30%), and 54.39% of the land use changes increased the value of ecosystem services.

A Framework for Real-Time AI-Driven Secure Code Analysis Integrated with DevSecOps in Cloud-Native CI/CD Pipelines

Abstract—This paper presents a novel framework for inte- grating real-time AI-driven secure code analysis into DevSecOps practices within cloud-native CI/CD pipelines. As organizations increasingly adopt cloud- native architectures and agile develop- ment methodologies, the need for robust, automated security mea- sures becomes paramount. Our proposed framework leverages advanced machine learning algorithms to perform continuous, real-time code analysis, identifying potential vulnerabilities and security risks throughout the development lifecycle. By seamlessly integrating with existing CI/CD tools and cloud platforms, our solution enables organizations to enforce security policies, detect threats, and remediate issues without compromising development velocity. We evaluate the effectiveness of our framework through a series of case studies across diverse software projects, demon- strating significant improvements in threat detection accuracy, reduced false positives, and overall security posture. Our results indicate that the proposed AI-driven approach can enhance code security by up to 40% compared to traditional static analysis tools, while maintaining the agility of modern development prac- tices. This research contributes to the evolving field of DevSecOps by offering a scalable, intelligent solution for embedding security into the heart of cloud-native software development processes. Index Terms—DevSecOps, AI-driven security, cloud- native, CI/CD pipelines, secure code analysis

A Security Situation Prediction Model for Industrial Control Network Based on Explainable Belief Rule Base

Industrial Control Systems (ICSs) are vital components of industrial production, and their security posture significantly impacts operational safety. Given that ICSs frequently interact with external networks, cyberattacks can disrupt system symmetry, thereby affecting industrial processes. This paper aims to predict the network security posture of ICSs to ensure system symmetry. A prediction model for the network security posture of ICSs was established utilizing Evidence Reasoning (ER) and Explainable Belief Rule Base (BRB-e) technologies. Initially, an evaluation framework for the ICS architecture was constructed, integrating data from various layers using ER. The development of the BRB prediction model requires input from domain experts to set initial parameters; however, the subjective nature of these settings may reduce prediction accuracy. To address this issue, an ICS network security posture prediction model based on the Explainable Belief Rule Base (BRB-e) was proposed. The modeling criteria for explainability were defined based on the characteristics of the ICS network, followed by the design of the inference process for the BRB-e prediction model to enhance accuracy and precision. Additionally, a parameter optimization method for the explainable BRB-e prediction model is presented using a constrained Projection Equilibrium Optimization (P-EO) algorithm. Experiments utilizing industrial datasets were conducted to validate the reliability and effectiveness of the prediction model. Comparative analyses indicated that the BRB-e model demonstrates distinct advantages in both prediction accuracy and explainability when compared to other algorithms.