Threat Scenarios Research Articles

A novel optimized deep learning based intrusion detection framework for an IoT networks

The burgeoning importance of Internet of Things (IoT) and its diverse applications have sparked significant interest in study circles. The inherent diversity within IoT networks renders them suitable for a myriad of real-time applications, firmly embedding them into the fabric of daily life. While IoT devices streamline various activities, their susceptibility to security threats is a glaring concern. Current inadequacies in security measures render IoT networks vulnerable, presenting an enticing target for attackers. This study suggests a novel dealing to address this challenge through the execution of Intrusion Detection Systems (IDS) leveraging superior deep learning models. Inspired by the benefits of Long Short Term Memory (LSTM), we introduce the Genetic Bee LSTM(GBLSTM) networks for the development of intelligent IDS capable of detecting a wide range of cyber-attacks targeting IoT area. The methodology comprises four key execution: (i) collection of unit for profiling normal IoT device behavior, (ii) Identification of malicious devices during an attack, (iii) Prediction of attack types implemented in the network. Intensive experimentations of the suggested IDS are conducted using various validation methods and prominent metrics across different IoT threat scenarios. Moreover, comprehensive experiments are conducted to evaluate the suggested models alongside existing learning models. The results demonstrate that the GBLSTM-models outperform other intellectual models in terms of accuracy, precision, and recall, underscoring their efficacy in securing IoT networks.

ASSESSMENT OF THE RISKS OF EMERGENCY SITUATIONS IN THE ENERGY SECTOR OF CRITICAL INFRASTRUCTURE

Problem statement. In order to effectively increase the security of critical infrastructure in the energy sector, it is necessary to assess all threats and analyze threat scenarios for the facility itself. Threats to energy security are the possibility of short-term or long-term situations, real or potential factors, phenomena or events that may disrupt the stability and security of the country's energy sector. These threats can lead to restriction or disruption of energy supply to consumers, accidents and other negative consequences. In many cases, the failure of one facility or system will affect the ability of interconnected facilities or systems in the same or another sector to perform their functions. For a relatively stationary system, it is important to identify those components or critical nodes where the potential consequences will be highest and where security and resilience efforts need to be focused. The purpose of the article is to develop and implement an approach to determining the risks of an emergency situation at critical infrastructure facilities using the example of the Dolyna Gas Processing Plant (GPP). Conclusions. An approach to identifying and assessing the risks of the critical infrastructure of the energy sector was developed and implemented, with the help of which potential vulnerabilities of the object to various threats are revealed. A simulation model for cascading effects has been built, which allows obtaining probabilistic estimates of the development of events under defined scenarios for a critical infrastructure object.

Leveraging Microsoft sentinel and logic apps for automated cyber threat response

An integrated approach to automated cyber threat response is explored in this paper, with Microsoft Sentinel's Security Information and Event Management (SIEM) capabilities being leveraged alongside Logic Apps' workflow automation within the Azure ecosystem. Efficient identification and automated mitigation of security incidents are enabled by a combination of AI-driven analytics and advanced threat-hunting capabilities, resulting in a substantial reduction of manual intervention and response times. The approach is demonstrated to contribute scientifically across three core areas: (1) a novel integration of Sentinel's SIEM with Logic Apps is proposed to streamline response workflows using automated playbooks; (2) the effectiveness of the system is assessed through multiple cyber threat scenarios, including automated account blocking and virtual machine isolation in response to identified threats; and (3) Sentinel's performance is evaluated relative to other SIEM solutions, such as Splunk and IBM QRadar, particularly in Azure-centric and hybrid environments. The potential of Microsoft Sentinel and Logic Apps to advance proactive cybersecurity defenses is underscored, while key limitations in scalability and cross-platform adaptability are also identified.

Justification Models of Organizational and Technical Support of Measures to Create Information Protection System of Informatization Objects

The relevance of the article is due to the need to protect information at all stages of creating an information system in the absence of appropriate organizational and technical support. To justify this kind of provision, mathematical models are needed that take into account the conditions and the random nature of the implementation of heterogeneous information processing processes in time at each stage, threats to its security and protection from these threats. The article discusses approaches to the development of such models based on the apparatus of flow theory and the theory of composite Petri ‒Markov networks, the possibilities of which for modeling the processes under study have not been previously considered. The purpose of the article. To reveal the content of issues related to the justification of organizational and technical support for information protection at the stages of creating an information system, to show the need and ways to quantify threats to its security and protection from these threats. For this purpose, the methods of functional and structural analysis, probability theory and flow theory were applied. In the course of solving the scientific problem, the relevance of substantiating the organizational and technical support of information protection at the stages of creating information systems is shown, descriptive models of its processing processes are developed, time diagrams of threat scenarios are given in the absence of protection measures and in the conditions of using preventive organizational and technical measures, indicators and analytical ratios for their calculation are proposed. The scientific novelty of the article consists in the fact that for the first time it examines the issues of organizational and technical support for information protection at the stages of creating an information system, examines the theoretical aspects of quantifying threats to its security and protection from these threats based on flow theory and prospects for applying the theory of composite Petri-Markov networks. Significance (theoretical). The conditions for the implementation of the studied processes and the possibility of applying the theory of flows and the apparatus of composite Petri–Markov networks for their modeling are established. Significance (practical). The results obtained in the work can be used to justify the organizational and technical provision of information protection at the stages of creating information systems of various organizations (both state and non-state), for which the implementation of threats at these stages may lead to damage to their activities.

Hybrid Prompt Learning for Generating Justifications of Security Risks in Automation Rules

Trigger-action platforms (TAPs) enable users without programming experience to personalize the behavior of Internet of Things applications and services through IF-THEN rules. Unfortunately, the arbitrary connection of smart devices and online services, even with simple rules, such as “IF the entrance Netatmo Wheather Station detects a temperature above 30 \({}^{\circ}C\) ( \(86^{\circ}F\) ) THEN open the shutters in the living room,” might expose users to potential security and privacy risks (e.g., the execution of the previous rule might provide an easy entry point for thieves, especially during the summer vacation period). The goal of our research is to make the users capable of understanding and mitigating the threats and risks associated with the execution of IF-THEN rules. To this end, we define a new challenging task, namely generating post hoc justifications of privacy and security risks associated with automation rules, and propose a novel natural language generation strategy based on hybrid prompt learning producing justifications in the form of real-life threat scenarios. The proposed strategy allows for prompt customization with task-specific information, providing contextual details enabling to grasp the nuances and subtleties of the domain language, resulting in more coherent justifications. The experiments conducted on the if-this-then-that (IFTTT) platform show that our method produces effective justifications, improving the explainability of discrete and hybrid prompting methods up to 27% in BLEURT score. The code of the software is publicly available on GitHub 1 .

Methods for Assessing the Effectiveness of Modern Counter Unmanned Aircraft Systems

Given the growing threat posed by the widespread availability of unmanned aircraft systems (UASs), which can be utilised for various unlawful activities, the need for a standardised method to evaluate the effectiveness of systems capable of detecting, tracking, and identifying (DTI) these devices has become increasingly urgent. This article draws upon research conducted under the European project COURAGEOUS, where 260 existing drone detection systems were analysed, and a methodology was developed for assessing the suitability of C-UASs in relation to specific threat scenarios. The article provides an overview of the most commonly employed technologies in C-UASs, such as radars, visible light cameras, thermal imaging cameras, laser range finders (lidars), and acoustic sensors. It explores the advantages and limitations of each technology, highlighting their reliance on different physical principles, and also briefly touches upon the legal implications associated with their deployment. The article presents the research framework and provides a structural description, alongside the functional and performance requirements, as well as the defined metrics. Furthermore, the methodology for testing the usability and effectiveness of individual C-UAS technologies in addressing specific threat scenarios is elaborated. Lastly, the article offers a concise list of prospective research directions concerning the analysis and evaluation of these technologies.

Cyber Security Assessment of An Interoperable Port Call and Voyage Optimization tool

Abstract The MISSION project aims to revolutionize maritime transport by developing a digital tool for real-time optimization of port calls and voyages, thereby reducing fuel consumption, cutting greenhouse gas emissions, and decreasing waiting times through enhanced coordination and information sharing among stakeholders. However, the security of the involved IT systems is critical to ensure safe and reliable operations. This paper introduces harborLang, a novel threat modeling language tailored for the maritime sector, built on the Meta Attack Language (MAL) framework. harborLang addresses the unique security challenges in maritime transport by enabling the modeling and mitigation of potential threats through detailed attack simulations. By integrating harborLang with the Yet Another Cybersecurity Risk Assessment Framework (YACRAF), the project enhances its risk analysis capabilities, allowing for precise threat scenarios that reflect the maritime environment’s complexities. The combined use of harborLang and YACRAF facilitates comprehensive cybersecurity risk assessments, significantly improving decision-making, operational safety, and the overall cybersecurity posture of maritime and port operations.

Alzheimer's pathology disrupts flexible place cell coding and hippocampal-prefrontal neural dynamics during risky foraging decisions in mice.

This study investigates the neural activity patterns associated with impaired decision-making in Alzheimer's disease (AD) by examining the effects of amyloid pathology on prefrontal-hippocampal circuit dynamics in 5XFAD mice, a model system known for its pronounced early amyloid pathology. Using ecologically relevant "approach food-avoid predator" paradigms, we revealed that 5XFAD mice display impaired decision strategies in risky foraging tasks, characterized by rigid hippocampal place fields and diminished sharp-wave ripple (SWR) frequencies, accompanied by disrupted prefrontal-hippocampal connectivity. These neural deficits align with behavioral inflexibility in dynamic threat scenarios. Our findings indicate that disrupted SWR dynamics and corticolimbic connectivity contribute to decision-making deficits in AD, emphasizing the potential of targeting these specific neural mechanisms to ameliorate cognitive impairments. This research contributes significantly to our understanding of AD's impact on cognition, providing insights that could lead to more targeted therapeutic strategies.

An integration methodology of safety and security requirements for autonomous vehicles

Safety and security co-engineering is one of the latest challenge in autonomous vehicle (AV) development. Efficiently integrating safety and security requirements during co-engineering is a new issue. Most functional safety and security analysis methods do not directly derive safety requirements, and improper handling of their relationship can affect system design and timelines. This article aims to use large language models (LLMs) to assist in the collaborative work of functional safety and security analysis. The main contributions are as follows: First, we propose three types of formulations to summarize hazard scenarios and threat scenarios and use LLMs to extract functional safety requirements and security requirements from them. Second, we utilized the three LLMs to perform relationship checks on the extracted functional safety requirements and security requirements. The results showed that the majority of the checks were correct and consistent, with only a small portion requiring manual intervention, significantly reducing human labor. Through these methods, we demonstrate the potential and efficiency of LLMs in the collaborative analysis of functional safety and security.

Memory-Efficient Trust Management for IoT Nodes Enabling Secure Communication in Trusted Environments

The Internet of Things (IoT) is changing quickly, so making sure that gadgets that are linked can talk to each other safely is very important. This paper describes a new way to handle trust that is specifically made for IoT nodes that work in safe settings. Conventional models frequently have a parcel of additional memory needs and are difficult to compute, which is made more regrettable by IoT gadgets that do not have a part of assets. Our proposed approach bargains with these issues by employing a belief administration framework that doesn't utilize a parcel of memory and finds an adjustment between security needs and the restricted assets that IoT hubs have. The foremost inventive thing around our strategy is that it can alter belief levels on the fly based on modern data and past experiences. Our framework takes up as small memory as conceivable whereas still having solid security measures since it employs lightweight cryptography conventions and quick information structures. This lets Internet of Things (IoT) gadgets utilize secure communication strategies without abating them down or making them less dependable. Context-aware belief assessment could be a key portion of our framework. This implies that belief levels are continually changed based on real-time encompassing variables and the way hubs are connected with each other. This adaptable alter makes beyond any doubt that belief choices remain in line with changing working conditions and threat scenarios. We moreover made beyond any doubt that our strategy works well with current IoT plans, permitting distinctive sorts of systems to work together and develop. We demonstrate that our system works in a wide range of IoT application cases by running a part of models and real-world tests on it. This study shows an easy-to-use and expandable way to improve trust management in IoT settings, with a focus on memory efficiency without losing safety. By using our approach, IoT stakeholders can reduce the risks that come with old trust models. This will make IoT environments more reliable and adaptable in the future.

Rule-Based Threat Analysis Methodology for Cyber-Physical Systems in Healthcare

Cyber-physical system (CPS) integration in the field of contemporary healthcare has completely changed patient monitoring and management. But because these systems are linked, they are vulnerable to different kinds of cyber attacks, which calls for strong security protocols. This work aims to proactively identify and reduce possible security risks by proposing a Rule- Based Threat Analysis Methodology (RBTAM) specifically designed for CPS in healthcare environments. RBTAM uses formalized rules to methodically evaluate threats in the physical and cyberspaces. Its foundations are in discrete mathematics. The process starts with identifying the parts and vulnerabilities of the system, then rules capturing possible threat scenarios are formulated. Among the many things covered by these regulations are denial of service, hardware malfunctions, data tampering, and unauthorized access. By means of an extensive analysis process, RBTAM assesses the probability and consequences of threats found on the availability, confidentiality, and integrity of healthcare systems. Through the facilitation of the prioritization of mitigation measures, this analysis helps healthcare professionals to efficiently distribute resources and reduce possible hazards to data security and patient safety. Important elements of RBTAM are the creation of threat scenarios specific to healthcare settings, the development of security rules based on system architecture, and the incorporation of real-time monitoring systems to identify and address new threats. Furthermore, the approach stresses feedback loops for ongoing improvement, which guarantees flexibility to changing cyberthreats and technology developments. We provide a case study of RBTAM application in a simulated CPS healthcare setting to illustrate its effectiveness. Results show that by efficiently identifying and reducing possible security risks, RBTAM improves the general resistance of healthcare systems to cyberattacks. In the ever-connected healthcare environment, RBTAM provides healthcare professionals and system administrators the tools and insights they need to protect patient well-being and data integrity through a proactive and methodical approach to threat analysis in CPS healthcare.

Possible detection of atmospheric bioaerosol via LiDAR: a wavelength-based simulation study

This study explores potential of LiDAR technology to rapidly detect aerosolized biological terror agents in the atmosphere. It assesses the application by simulating extinction coefficients and the Ångström exponent at various wavelengths (266, 1064, 1571, and 2000 nm), focusing on differentiating bioaerosols from typical atmospheric particles. The simulation analysis evaluates changes in aerosol distributions and related extinction coefficient and Ångström exponent shifts under clean, normal, and bad atmospheric conditions. The findings indicate that the 1064 nm wavelength effectively detects bioaerosol presence, with a combination of 1064 nm and 1571 nm providing optimal Ångström exponent use for particle size differentiation. This dual-wavelength approach is highlighted as a practical method for bioaerosol detection, showcasing a significant sensitivity to variations in particle quantity and size, which are critical in biological threat scenarios. In conclusion, the study offers guidance for selecting LiDAR wavelengths for biological agent detection systems. While providing a theoretical framework for practical applications, it also underlines the need for further experimental work to confirm findings and fine-tune technology for real-world monitoring and threat management. This research contributes to the development of effective monitoring strategies against the backdrop of biological terror threats.Graphical

Collaboration with the Disaster Resilient Village Forum in Disaster Emergency Response Simulation in Ngelang Kartoharjo Village Magetan.

The problem faced by the Disaster Resilient Village Forum (Destana) in Ngelang Village, Kartoharjo District, Magetan is the difficulty in realizing community-based disaster preparedness, especially simulations of disaster emergency response. This community service activity is a follow-up to the results of research on priority parameters for realizing disaster preparedness using the Quadrant of Difficulty and Usefulness Preparedness method. The activity aims to improve the capabilities and skills of the Destana Forum in organizing disaster emergency response simulations, as well as building community resilience in facing disaster threats. The activity method is Participatory Action Research, which is applied through training and field practice. Participants were the administrators of the Destana Forum, elements of the Village Development Officer (Babinsa), Bhayangkara elements of Community Security and Order (Bhabinkamtibmas), and the community, with a total of 112 people. Activities were carried out on May 13 to May 17, 2024, in Ngelang Village, Kartoharjo District, Magetan. The results of the activity showed that the participants had succeeded in compiling and providing contingency plan documents, compiling threat scenarios, impact scenarios, and disaster event scenarios, as well as preparing operational rehearsal plans, conducting room rehearsals, and ending with a practical field rehearsal for disaster emergency response simulations. The conclusion from this activity is that strengthening community-based disaster preparedness needs to be realized through collaboration and participatory assistance.

Боротьба з контрабандою: заходи та виклики

Ukraine, as a modern state, faces the challenge of effectively combating smuggling, which not only leads to significant budget losses but also poses a threat to national security and economic development. This article discusses the key aspects of this problem, from the causes of smuggling of equipment to the low level of incentives for customs officers and the threats they face. The reasons for smuggling are discussed, such as corruption, excessive customs duties and taxes, and imperfect legislation. The article also considers the need for parliamentary and public control over the actions of customs and border authorities, as well as optimization of taxation to ensure effective protection of the domestic market. Particular emphasis is placed on the importance of international cooperation, technical support, dog training and public education for the successful fight against smuggling. The article also discusses the problem of low salaries and poor incentives for customs officers, which can jeopardize the effectiveness of the fight against smuggling. The article concludes with a section on possible threats to customs officials and ways to address them, including the possibility of threats, bribery and other negative influences that complicate their work. This article also discusses comprehensive measures such as technical support for customs, the development of a dog service, and the importance of public education for the successful fight against smuggling. The low level of salaries among customs officers and their weak incentives to disclose smuggling schemes are seen as a systemic problem that requires a comprehensive approach and solution. The author analyzes the threats that may be directed at customs officials by smugglers and criminal elements. Scenarios of influence, possible bribery, threats and other negative interactions are discussed in order to ensure effective measures to protect customs and border services. All these aspects interact and influence the success of the fight against smuggling in Ukraine. The article highlights the need for a systematic approach and cooperation of all stakeholders – government agencies, the public and international partners – to create an effective system that minimizes the risks and losses from smuggling.

Enhancement Network Security Features Through Intrusion Detection Systems

Abstract: In pursuit of its objectives, the project "Enhancement Network Security features through Intrusion Detection Systems" undertakes a comprehensive exploration of the intricacies surrounding network security, delving into the nuances of intrusion detection methodologies. By dissecting the mechanisms behind both anomaly- and signature-based IDS, the initiative aims not only to implement these systems but also to grasp their underlying principles deeply. Through rigorous experimentation and analysis, the project endeavours to uncover the strengths and limitations of each approach, thereby paving the way for the development of a hybrid system that leverages the best of both worlds. This meticulous approach ensures that the resulting IDS is not only robust but also finely tuned to adapt to the ever-evolving landscape of cyber threats. Furthermore, the integration of machine learning techniques represents a pivotal advancement in the realm of intrusion detection. By harnessing the power of algorithms capable of autonomously learning from data, the IDS transcends static rule-based detection methods, ushering in a new era of adaptive security measures. Through continuous training on vast datasets comprising diverse threat scenarios, the machine learning-enabled IDS hones its ability to discern subtle patterns indicative of malicious activity, thus bolstering its efficacy in safeguarding network assets. Moreover, the implementation of machine learning algorithms fosters a proactive stance against emerging threats, enabling the system to anticipate and mitigate potential breaches before they materialize into fullfledged attacks. This proactive approach not only enhances the overall security posture of organizations but also instils confidence in their ability to stay ahead of the curve in an increasingly hostile digital landscape. The project's goal is to provide companies with a stronger protection against cyberattacks by guaranteeing the availability, confidentiality, and integrity of their networked systems.

Stealth Aircraft Penetration Trajectory Planning in 3D Complex Dynamic Based on Radar Valley Radius and Turning Maneuver

Based on the quasi-six-degree-of-freedom flight dynamic equations, considering the changes in the elevation angle caused by an increase in the rolling angle during maneuvering turns, which leads to a rise in the radar cross-section. A computational model for the radar detection probability of aircraft in complex environments was constructed. By comprehensively considering flight parameters such as turning angle, rolling angle, Mach number, and radar power factor, this study quantitatively analyzed the influence of these factors on the radar detection probability. It revealed the variation patterns of radar detection probability under different flight conditions. The results provide theoretical support for the Radar Valley Radius and Turning Maneuver Method (RVR-TM) based on decision trees, and lay the foundation for the development of subsequent intelligent decision-making models. To further optimize the trajectory selection of aircraft in complex environments, this study combines theoretical analysis with reinforcement learning algorithms to establish an intelligent decision-making model. This model is trained using the Proximal Policy Optimization (PPO) algorithm, and through precisely defining the state space and reward functions, it accomplishes intelligent trajectory planning for stealth aircraft under radar threat scenarios.

Collective order and group structure of shoaling fish subject to differing risk-level treatments with a sympatric predator.

It is imperative for individuals to exhibit flexible behaviour according to ecological context, such as available resources or predation threat. Manipulative studies on responses to threat often focus on behaviour in the presence of a single indicator for the potential of predation, whereas in the wild perception of threat will probably be more nuanced. Here, we examine the collective behaviour of eastern mosquitofish (Gambusia holbrooki) subject to five differing threat scenarios relating to the presence and hunger state of a jade perch (Scortum barcoo). Across threat scenarios, groups exhibit unique behavioural profiles that differ in the durations that particular collective states are maintained, the probability of transitions between states, the size and duration of persistence of spatially defined subgroups, and the patterns of collective order of these subgroups. Under the greatest level of threat, subgroups of consistent membership persist for longer durations. Group-level behaviours, and their differences, are interconnected with differences in estimates of the underlying rules of interaction thought to govern collective motion. The responses of the group are shown to be specific to the details of a potential threat, rather than a binary response to the presence or absence of some form of threat.

Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual Reality: Robustness and User Experience.

Eye tracking is routinely being incorporated into virtual reality (VR) systems. Prior research has shown that eye tracking data, if exposed, can be used for re-identification attacks [14]. The state of our knowledge about currently existing privacy mechanisms is limited to privacy-utility trade-off curves based on data-centric metrics of utility, such as prediction error, and black-box threat models. We propose that for interactive VR applications, it is essential to consider user-centric notions of utility and a variety of threat models. We develop a methodology to evaluate real-time privacy mechanisms for interactive VR applications that incorporate subjective user experience and task performance metrics. We evaluate selected privacy mechanisms using this methodology and find that re-identification accuracy can be decreased to as low as 14% while maintaining a high usability score and reasonable task performance. Finally, we elucidate three threat scenarios (black-box, black-box with exemplars, and white-box) and assess how well the different privacy mechanisms hold up to these adversarial scenarios. This work advances the state of the art in VR privacy by providing a methodology for end-to-end assessment of the risk of re-identification attacks and potential mitigating solutions. f.

Design and Development of Android App Malware Detector API Using Androguard and Catboost

Abstract: Android has been constant target of attacks through the use of malicious applications (malware) that can harm users, for instance, by leaking sensitive data (e.g., bank account details), blocking access to information and demanding monetary compensation for the ransom, or even leveraging social engineering scams. A large number of malicious applications is distributed in a daily basis through different distribution vectors, such as application markets, including official and third party stores, or untrusted sources like Web repositories. The increasing prevalence of Android malware necessitates advanced detection mechanisms to safeguard users and their devices. The aim of the project is to presents an innovative approach to Android malware detection, integrating machine learning, blockchain technology, and web development tools. The proposed system is to develop a Andriod App Malware Detector API capable of identifying and mitigating malicious applications effectively. The system begins with the collection of diverse datasets comprising both benign and malicious Android applications, ensuring the representation of various threat vectors and scenarios. Androguard, a powerful tool for Android app analysis, the system extracts relevant features such as permissions, API calls, and intents, providing valuable insights into the behaviour and characteristics of each application. The AndroMal Model is designed and trained using CatBoost Algorithm and integrated with a blockchain framework for secure metadata storage. Consensus mechanisms and smart contracts enhance transparency and reliability. Privacy considerations, continuous monitoring, and user feedback mechanisms further fortify the system. The proposed design promises an effective, scalable, and user-centric solution for Android malware detection.

Beyond Conventional Threat Defense: Implementing Advanced Threat Modeling Techniques, Risk Modeling Frameworks and Contingency Planning in the Healthcare Sector for Enhanced Data Security

This research study explores the integration of advanced threat and risk modeling with scenario and contingency planning to enhance security and resilience within healthcare organizations. Given the escalating complexity and frequency of cyber threats targeting the healthcare sector, the study assesses the efficacy of a synergistic cybersecurity approach. Utilizing a quantitative research methodology, data were collected through a survey administered to 452 healthcare practitioners, focusing on their perceptions and experiences with cybersecurity threats, risk modeling techniques, and the effectiveness of scenario and contingency planning. The survey incorporated a structured questionnaire utilizing Likert scale closed-ended questions, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed four hypotheses related to the impact of advanced threat and risk modeling and scenario and contingency planning on the cybersecurity posture and operational resilience of healthcare organizations. Results indicated a significant positive relationship between advanced threat modeling and scenario planning with the cybersecurity posture of healthcare organizations, which, in turn, notably enhances organizational resilience. Specifically, the study found that integrating advanced threat and risk modeling with scenario and contingency planning significantly improves the ability of healthcare organizations to identify, assess, and mitigate cyber threats effectively. Furthermore, the findings suggest that such integration contributes to more informed decision-making, improved information security management practices, and more effective and efficient digital crime investigation processes. The research study concludes that a comprehensive approach integrating advanced threat and risk modeling with scenario and contingency planning significantly enhances healthcare organizations' cybersecurity posture and operational resilience. The study recommends adopting an integrated cybersecurity strategy, continuous cybersecurity education for all organizational members,regular cybersecurity assessments, and fostering collaboration for enhanced cybersecurity intelligence.