Security Posture Research Articles

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.

A hybrid approach using support vector machine rule-based system: detecting cyber threats in internet of things

While the proliferation of the Internet of Things (IoT) has revolutionized several industries, it has also created severe data security concerns. The security of these network devices and the dependability of IoT networks depend on efficient threat detection. Device heterogeneity, computing resource constraints, and the ever-changing nature of cyber threats are a few of the obstacles that make detecting cyber threats in IoT systems difficult. Complex threats often go undetected by conventional security measures, requiring more sophisticated, adaptive detection methods. Therefore, this study presents the Hybrid approach based on the Support Vector Machines Rule-Based Detection (HSVMR-D) method for an all-encompassing approach to identifying cyber threats to the IoT. The HSVMR-D employs SVM to categorize known and unknown threats using attributes acquired from IoT data. Identifying known attack signatures and patterns using rule-based approaches improves detection efficiency without retraining by adapting pre-trained models to new IoT contexts. Moreover, protecting vital infrastructure and sensitive data, HSVMR-D provides a thorough and adaptable solution to improve the security posture of IoT deployments. Comprehensive experiment analysis and simulation results compared to the baseline study have confirmed the efficiency of the proposed HSVMR-D. Furthermore, increased resilience to completely novel changing threats, fewer false positives, and improved accuracy in threat detection are all outcomes that show the proposed work outperforms others. The HSVMR-D approach is helpful where the primary objective is a secure environment in the Internet of Things (IoT) when resources are limited.

The Evolution of Identity and Access Management: Integrating Biometric and Behavioral Authentication

This article explores the transformative role of biometric and behavioral authentication in the evolving landscape of Identity and Access Management (IAM). As traditional password-based systems increasingly fail to meet the challenges posed by sophisticated cyber threats, these advanced authentication methods offer a promising solution. The article provides a comprehensive overview of physical and behavioral biometrics, examining their integration into multi-factor authentication systems and their capacity for continuous, passive user verification. It delves into the significant security benefits these technologies offer, including enhanced resistance to phishing, social engineering, and insider threats, while also highlighting the improvements in user experience and convenience. The article critically analyzes the challenges and considerations associated with implementing biometric and behavioral authentication, including privacy concerns, data security, accuracy issues, and scalability. Furthermore, it explores future directions and research opportunities in the field, such as advancements in biometric technologies, the application of machine learning and AI in behavioral analysis, and the development of context-aware authentication systems. By synthesizing current research and industry trends, this article provides valuable insights for cybersecurity professionals, IT decision-makers, and researchers interested in the future of secure and user-friendly authentication methods. It underscores the potential of biometric and behavioral authentication to significantly enhance organizational security postures while improving user satisfaction in an increasingly digital world.

Cyber risk logics and their implications for cybersecurity

Abstract Cybersecurity in national and international security is frequently discussed in an existential register. However, most cybersecurity activities are normal and routine, including diverse practices of cyber risk management. The intricacies of cyber risk and its connection to security and threat politics have received surprisingly little attention in the cyber politics literature. This article addresses this gap through a twofold theoretical proposition. The first argues that cyber risk in policy and practice inhabits a continuum between ‘classical’ risk and security postures. The second proposes the existence of multiple risk logics, located at different points along this continuum. To illustrate this, we outline two distinct cyber risk logics: ‘risk as potential threats’ and ‘risk as uncertainty’. Through an exploratory case-study of cyber risk policy and guidance in the United Kingdom, we find indications of the simultaneous existence of these risk logics, including in specific organizational contexts. We propose that the ‘risk as potential threats’ logic, in particular, acts as a ‘bridge’ between conventional risk and security. We conclude by discussing how differentiating cyber risk logics facilitates a more finely grained appreciation of cybersecurity policy and practice and provides opportunities for disciplinary engagement with the organizational and institutional politics of cybersecurity and ‘the international’.

Incident response: A structured model from detection to containment and recovery

As cyber-attacks evolve in sophistication; organizations are under constant threat. This necessitates a cohesive approach to prioritize incident response (IR) capabilities and mitigate potential damages. This research paper explores integrating Information Security Management (ISM) and Incident Response (IR) functions; underlining the need for a unified strategy that leverages organizational learning theory. The study comprehensively analyzes the Incident Response Lifecycle; outlining the critical phases of preparation; detection and analysis; containment; eradication; recovery; and post-incident activities. It also investigates the crucial role and structure of Incident Response Teams (IRTs); advocating for tailored team formations that adapt to the dynamic nature of cyber incidents. By fostering collaboration between ISM and IR functions and focusing on technical and socio-technical factors; organizations can enhance their resilience against cyber threats and improve their overall security posture.

Nist Aal Adaptive Security Framework

In our digitally evolving security world, adaptive and risk - based authentication methods are the key countermeasures responding to the current rising cyber risks. The National Institute of Standards and Technology (NIST) Authenticator Assurance Levels (AAL) framework offers a specific guidance on the implementation of adaptive security measures. The study is designed to put the "NIST AAL Adaptive Security Framework" into practice by using technologies such as Ping Federate and Ping Davinci systems for the timely risk analysis of IT environments and advanced MFA approaches against emerging threats. AAL model can define authentication procedures customized as per the level of risk, requirements and compliance regulations of the organization. It is essential to classify authentication into separate assurance level (AAL1, AAL2 and AAL3) as it helps to make modifications according to contextual factors like perceived risk and user's behaviour. This is vital as it enhances security while it optimizes user experience. This study examines the proposed framework’s efficacy in reducing cyber - threats and strengthening security posture of organizations.

Malware Detection Using Artificial Intelligence: Techniques, Research Issues and Future Directions

Artificial intelligence (AI) is an effective technology used for upgrading the security posture against a variety of security challenges and cyber-attacks that cyber security teams may use. Malware is a software which aims to access a device without the explicit permission of its owner. Forensics investigations report that many organizations have encountered unusual records, collected by their antiviral security monitoring systems. Most of their arrangements skeptically pass a large amount of diplomatic data through various unethical strategies that make malware identification tougher. However, these procedures have varied limitations that call for an unused inquiry about the track. This study explores the complex relationship between malware detection and AI [1]. This paper provides insights into performance evaluation metrics and discusses several research issues that impede the effectiveness of existing techniques. The study also provides recommendations for future research directions and is a valuable resource for researchers and practitioners working in the field of malware detection.

Cybersecurity Transformation: Cyber-Resilient IT Project Management Framework

In response to the escalating threats of cybersecurity attacks and breaches, ensuring the development and deployment of secure IT products has become paramount for organizations in their cybersecurity transformation. This work emphasizes the critical need for a comprehensive and secure IT project management life cycle that safeguards products from their initial development stages through decommissioning. The primary objective is to seamlessly integrate security considerations into every facet of IT project management life cycles. This work embraces a cyber-resilient IT project management framework and advocates the inclusion of cybersecurity measures in IT projects and their strategic, organized, continuous, and systematic integration throughout the entire product life cycle. It introduces a pioneering framework that harmonizes the cybersecurity risk management process with the IT project management life cycle. This framework delineates a methodical sequence of steps, each encompassing a distinct set of activities. The effectiveness and practical applicability of the proposed framework were validated through a comprehensive case study focused on the Personal Health Record (PHR) system. The PHR case study served as a real-world scenario to assess the framework’s ability to address cybersecurity challenges in a specific domain. The results of the experiment demonstrated the framework’s efficacy in enhancing the security posture of IT projects, showcasing its adaptability and scalability across diverse applications.

A Novel Cloud-Enabled Cyber Threat Hunting Platform for Evaluating the Cyber Risks Associated with Smart Health Ecosystems

The fast proliferation of Internet of Things (IoT) devices has dramatically altered healthcare, increasing the efficiency and efficacy of smart health ecosystems. However, this expansion has created substantial security risks, as cybercriminals increasingly target IoT devices in order to exploit their weaknesses and relay critical health information. The rising threat landscape poses serious concerns across various domains within healthcare, where the protection of patient information and the integrity of medical devices are paramount. Smart health systems, while offering numerous benefits, are particularly vulnerable to cyber-attacks due to the integration of IoT devices and the vast amounts of data they generate. Healthcare providers, although unable to control the actions of cyber adversaries, can take proactive steps to secure their systems by adopting robust cybersecurity measures, such as strong user authentication, regular system updates, and the implementation of advanced security technologies. This research introduces a groundbreaking approach to addressing the cybersecurity challenges in smart health ecosystems through the deployment of a novel cloud-enabled cyber threat-hunting platform. This platform leverages deception technology, which involves creating decoys, traps, and false information to divert cybercriminals away from legitimate health data and systems. By using this innovative approach, the platform assesses the cyber risks associated with smart health systems, offering actionable recommendations to healthcare stakeholders on how to minimize cyber risks and enhance the security posture of IoT-enabled healthcare solutions. Overall, this pioneering research represents a significant advancement in safeguarding the increasingly interconnected world of smart health ecosystems, providing a promising strategy for defending against the escalating cyber threats faced by the healthcare industry.

Undersea cable security in the Indo‐Pacific: Enhancing the Quad’s collaborative approach

Undersea communication cables carry the bulk of global communications, intelligence, and financial transactions daily. They are increasingly threatened, and Australia, Japan, India, and the US have begun coordinating cable security activities in the Indo-Pacific as the Quad. This article investigates the Quad’s role in undersea cable communication security, and analyzes its motivations, strengths, and the challenges it faces thereby illuminating the Quad’s evolving position in international security and its role in global governance. In doing so, it evaluates the Quad’s significance in addressing emerging security threats in the Indo-Pacific, attempts to fill critical gaps in policy formulation, and contributes to theories of informality and global governance. It begins by outlining threats to undersea communication cables. Second, it details the Quad Partnership for Cable Connectivity and Resilience within the context of an informal intergovernmental organization literature. Third, it provides recommendations for policy formulation in undersea cable security and explores the Quad's potential for improving connectivity, resilience, and security. Finally, the article emphasizes the need to tailor policies to India’s unique position within the Quad and explores proactive measures like floating Quad-supported cable repair ships. These recommendations offer insights for policymakers to bolster global communication infrastructure, resilience, and provide public goods amidst multipolarity and great power rivalry.

Integrating Security Information and Event Management (SIEM) with Data Lakes and AI: Enhancing Threat Detection and Response

The evolving threat landscape in cybersecurity necessitates more advanced and efficient solutions for threat detection and response. Traditional Security Information and Event Management (SIEM) systems have limitations in handling large volumes of data and identifying sophisticated threats. This research explores the integration of SIEM solutions with data lakes, offering a scalable and flexible approach to managing security data. By leveraging artificial intelligence (AI) and machine learning (ML) algorithms, SIEM platforms can enhance their capabilities in real-time threat detection, automated response, and advanced analytics. This integration enables organizations to process vast amounts of structured and unstructured data from various sources, improving both the speed and accuracy of identifying security threats. The article examines the architecture, benefits, and challenges of combining SIEM with data lakes and AI, providing insights into how these technologies can collectively strengthen organizational security postures.

Hybrid Intelligent Anomaly Detection System Using Attention based Deep Learning Approach for Cyber Attacks Prevention

Objectives: Network Intrusion Detection System (NIDS) plays an important role in finding and preventing cyber-attacks, which helps to improve the entire security posture of an organization’s network infrastructure. The development of Deep Learning (DL) techniques possess the ability of IDS to detect attacks without delay and protects from intrusions even in real-time environment. Methods: The present study proposes an improved IDS framework called Enhanced Gated Recurrent Unit Hyper-Model combined Attention Bidirectional Long-Short Term Memory (EGHAB) approach, to effectively address the detection of attacks with maximum accuracy and minimal error rate. The proposed model is enhanced by using methods like numericalization and normalisation for pre-processing the input data, Genetic Algorithm (GA) for extracting intricate features from the input data, and the EGHAB classifier, which works on the principles of both Gated Recurrent Unit (GRU) and Bidirectional Long-Short Term Memory (BiLSTM) models along with attention mechanism. Findings: The EGHAB model efficiently learns the abnormal behaviour of network and classified the attack and non-attack data using the publicly available NSL-KDD dataset and a generated real time data set, scapy. And it achieved 99.94% accuracy over NSL KDD dataset during multiclass classification and 93.3% on real time data set with reduced Error rate. Additionally, to examine the efficacy, the proposed approach is compared with other existing methods and proved its improved performance. Novelty: A combined ensemble classifier with GRU, BiLSTM and attention mechanism is designed to predict the attacks in earlier stage rather than due over. The model achieved better accuracy and reduced false assumption using anomaly prediction mechanism. Keywords: Cyber Attack, Anomaly Detection, Deep learning, Gated Recurrent Unit, Bi­ Directional Long­-Short Term Memory, Attention mechanism

Holistic Information Security Management and Compliance Framework

The growing complexity of cybersecurity threats demands a robust framework that integrates various security domains, addressing the issue of disjointed security practices that fail to comply with evolving regulations. This paper introduces a novel information security management and compliance framework that integrates operational, technical, human, and physical security domains. The aim of this framework is to enable organizations to identify the requisite information security controls and legislative compliance needs effectively. Unlike traditional approaches, this framework systematically aligns with both current and emerging security legislation, including GDPR, NIS2 Directive, and the Artificial Intelligence Act, offering a unified approach to comprehensive security management. The experimental methodology involves evaluating the framework against five distinct risk scenarios to test its effectiveness and adaptability. Each scenario assesses the framework’s capability to manage and ensure compliance with specific security controls and regulations. The results demonstrate that the proposed framework not only meets compliance requirements across multiple security domains but also provides a scalable solution for adapting to new threats and regulations efficiently. These findings represent a significant step forward in holistic security management, indicating that organizations can enhance their security posture and legislative compliance simultaneously through this integrated framework.

Minimizing Business Disruption During Enterprise End-Point OS Upgrade while Maintaining a Robust Security Posture

The deployment of a major end-point operating system (OS) upgrade presents significant challenges for enterprise organizations, requiring a comprehensive strategy to minimize business disruption and maintain a robust security posture. Beyond the operational complexities of managing user productivity and application compatibility, organizations must also proactively address the critical security risks associated with such large-scale technology transitions. Cybercriminals often target organizations undergoing major IT changes, seeking to exploit vulnerabilities that may arise due to shifts in the OS architecture, user workflows, and security controls. This paper outlines practical strategies for executing successful OS upgrade projects that balance operational resilience and cyber risk management. Key focus areas include proactive management of end-point security, comprehensive contingency planning and rollback procedures, strategies to sustain productivity and business continuity, effective end-user training and communication, and valuable insights into the next- generation OS deployment. By focusing on these critical aspects, organizations can navigate major OS upgrade initiatives while preserving vigorous business operations, enhancing end-user experience, and safeguarding sensitive data and systems from costly security incidents.

From Dis-empowerment to empowerment: Crafting a healthcare cybersecurity self-assessment

Due to the valuable and sensitive nature of its data, the Australian healthcare sector is increasingly targeted by cyberattacks. Existing cybersecurity evaluation methods often lack the specificity required to address the unique vulnerabilities within this sector, especially in terms of engaging stakeholders and fostering a proactive security culture. These evaluations often overlook psychological empowerment, which enhances individuals’ confidence in managing cybersecurity.This study aims to develop a tailored cybersecurity self-assessment index for the Australian healthcare system. It will focus on enhancing psychological empowerment alongside technical assessments to improve overall sector resilience against cyber threats.Using a design science research approach, the index was developed using expert reviews, online surveys, and in-depth interviews with key stakeholders, including healthcare providers, consumers, and government entities. This iterative process involved identifying gaps in existing cybersecurity measures and designing an index to address technical and human factors.The index’s evaluation through a pilot study revealed that it effectively raised awareness and empowered individuals within the healthcare sector to take ownership of cybersecurity practices. Participants reported increased confidence in managing cybersecurity risks and found the index’s actionable recommendations helpful in improving their security posture. However, challenges related to its applicability across diverse healthcare environments and regulatory constraints were identified.The Australian Healthcare Cybersecurity Self-Assessment Index shows promise as a tool for strengthening cybersecurity in the healthcare sector by integrating psychological empowerment with technical assessments. Further research is needed to refine the tool, incorporate quantitative data, and explore its scalability across different healthcare settings and global applications.

IOT NETWORK INTRUSION DETECTION USING MACHINE LEARNING ON UNSW-NB15 DATASET

This research presents a comprehensive investigation into the application of machine learning techniques for addressing the pervasive security challenges within Internet of Things (IoT) networks. With the exponential growth of interconnected devices, ensuring the integrity and confidentiality of data transmissions has become increasingly critical. In this study, we deploy and evaluate seven distinct machine learning methods tailored to the IoT network intrusion detection problem. Leveraging the rich and diverse UNSW-NB15 dataset, encompassing real-world network traffic scenarios, our analysis encompasses a thorough examination of both traditional and state-of-the-art algorithms. Through rigorous experimentation and performance evaluation, we assess the efficacy of these methods in accurately detecting and classifying various forms of network intrusions. Our findings provide valuable insights into the strengths and limitations of different machine learning approaches for enhancing the security posture of IoT environments, thereby facilitating informed decision-making for network administrators and cybersecurity practitioners.

Secure Efficiency: Navigating Performance Challenges in Multi-Tenant Cloud Security Implementations

Multi-tenant cloud environments offer significant advantages in terms of cost-efficiency and scalability, but they also present unique challenges in balancing robust security measures with optimal performance. This article examines the intricate relationship between security implementations and system performance in shared cloud infrastructures. Through a comprehensive analysis of data isolation techniques, access control mechanisms, network security protocols, and threat detection systems, we identify key areas where security measures can impact performance metrics such as latency, throughput, and resource utilization. Our article employs a mixed-methods approach, combining quantitative performance measurements with qualitative case studies from industry-leading cloud service providers. The findings reveal that while stringent security measures often introduce performance overhead, strategic implementation and optimization can significantly mitigate these effects. We propose a framework for dynamically balancing security and performance requirements, incorporating emerging technologies such as AI-driven threat detection and automated resource allocation. This article contributes to the growing body of knowledge on cloud computing optimization and provides practical insights for cloud architects and security professionals seeking to enhance both the security posture and performance efficiency of multi-tenant cloud environments.

An Integrated Frequency Regulation Method Based on System Frequency Security Posture

As the share of renewable energy in a grid increases, the grid’s frequency support capability weakens, and the spatial distribution of grid frequency becomes more pronounced. As a result, control strategies based on system frequency consistency and traditional frequency regulation dominated by synchronous machines are becoming increasingly inadequate for meeting the frequency regulation requirements of new-type power systems. To enhance the system’s frequency support capability, it is imperative to fully utilize the frequency regulation resources within the power system. To address this issue, this paper first introduces a system frequency security posture assessment method that accounts for the spatial distribution characteristics of the grid. Subsequently, a parameter optimization method for diverse frequency regulation resources is proposed in conjunction with the proposed comprehensive evaluation method for frequency regulation. Next, using a renewable energy feeder regional grid as an example, an integrated frequency regulation method based on the system frequency security posture is presented. Finally, the frequency regulation performance and economic costs of different frequency regulation methods are analyzed under various operating scenarios and disturbances using a model based on actual data from the renewable energy feeder regional grid. The simulation and index calculation results demonstrate that the method proposed in this paper effectively enhances the system’s frequency support capability, reduces the frequency disparity between different nodes within the grid, and maintains high economic performance.

Cybersecurity in the Age of IoT: Business Strategies for Managing Emerging Threats

The quick spread of the Internet of Things (IoT) has essentially changed industries by linking devices, systems, and data. This expansion has, in turn, generated exceptional cybersecurity vulnerabilities, which introduces an urgent requirement for detailed security solutions. This paper analyzes new cybersecurity risks present in the IoT ecosystem and looks into successful business approaches for managing these threats. Using a thorough assessment of current literature, practical case studies, and extensive data analysis, the study discovers essential weaknesses in IoT devices, networks, and infrastructure. The method used collects information from latest incidents related to IoT breaches and evaluates several business strategies designed to meet these challenges. Findings imply that using advanced technologies like artificial intelligence (AI) and machine learning (ML) in monitoring, in conjunction with collaborative cybersecurity frameworks, strongly enhances the security of the IoT. The originality of this research is in its thorough review of the evolving threat landscape and its practical recommendations for enterprises to advance their Internet of Things security position in a quickly changing environment.

NOVEL STUDY ON ETHICAL HACKING AND VULNERABILITY TESTING

Ethical hacking and vulnerability testing represent critical components of modern cybersecurity strategies, aimed at proactively identifying and mitigating potential security threats within organizational systems and networks. Ethical hacking, also known as penetration testing, involves authorized attempts to exploit vulnerabilities in systems, applications, or networks, mirroring the tactics of malicious actors. This proactive approach allows organizations to uncover weaknesses before they are exploited by cybercriminals, thereby strengthening overall security posture. Vulnerability testing complements ethical hacking by systematically assessing systems for known vulnerabilities and configuration errors, providing insights into areas requiring remediation. Together, these practices enable organizations to assess, prioritize, and address security risks effectively, safeguarding sensitive data, maintaining regulatory compliance, and preserving business continuity. This article explores the principles, methodologies, and benefits of ethical hacking and vulnerability testing, emphasizing their role in enhancing cybersecurity resilience in an increasingly interconnected digital landscape. Keywords: Cybersecurity, Ethical Hacking, Mitigation, Networks, Penetration Testing, Security Threats, Systems, Testing, Vulnerabilities.