Threat Analysis Research Articles

Strengths, Weaknesses, Opportunities, and Threats Analysis of Integrating ChatGPT Into Nursing Education

Artificial intelligence (AI) is driving global change, and the implementation of generative AI in higher education is inevitable. AI language models such as the chat generative pre-trained transformer (ChatGPT) hold the potential to revolutionize the delivery of nursing education in the future. Nurse educators play a crucial role in preparing nursing students for a future technology-integrated healthcare system. While the technology has limitations and potential biases, the emergence of ChatGPT presents both opportunities and challenges. It is critical for faculty to be familiar with the capabilities and limitations of this model to foster effective, ethical, and responsible utilization of AI technology while preparing students in advance for the dynamic and rapidly advancing landscape of nursing and healthcare. Therefore, this article was written to present a strengths, weaknesses, opportunities, and threats (SWOT) analysis of integrating ChatGPT into nursing education, providing a guide for implementing ChatGPT in nursing education and offering a well-rounded assessment to help nurse educators make informed decisions.

THREAT MODEL FOR PAYMENT SYSTEMS

Payment systems are the backbone of modern economies, and their security is a critical aspect of maintaining user trust and safeguarding financial data. In this context, the development of threat models plays a central role in ensuring the protection of these systems from attacks. One of the most effective methodologies for threat modeling is STRIDE, which helps to structure and systematize the analysis of risks and threats. By utilizing the STRIDE model and incorporating a customized threat model for payment systems, the security of authentication processes and transaction handling can be significantly enhanced.

Selection, Planning, and Modelling of Nature-Based Solutions for Flood Mitigation

The use of nature-based solutions (NBSs) for hazard mitigation is increasing. In this study, we review the use of NBSs for flood mitigation using a strengths, weaknesses, opportunities, and threats (SWOT) analysis framework for commonly used NBSs. Approaches reviewed include retention and detention systems, bioretention systems, landcover and soil management, river naturalisation and floodplain management, and constructed and natural wetlands. Existing tools for identification and quantification of direct benefits and co-benefits of NBSs are then reviewed. Finally, approaches to the modelling of NBSs are discussed, including the type of model and model parameterisation. After outlining knowledge gaps within the current literature and research, a roadmap for development, modelling, and implementation of NBSs is presented.

Expert perspectives on disaster risk reduction strategies in the tourist area of Borobudur-Yogyakarta-Prambanan in Indonesia

Tourism plays a crucial role in the economic growth of many countries. However, tourist destinations located in disaster-prone areas face increased vulnerability in the event of natural disasters. It is essential for these areas to have specific interventions, plans, and policies that focus on reducing disaster risk, while ensuring the safety of residents and tourists. This study proposes strategies for minimizing disaster risk using Indonesia's Borobudur-Yogyakarta-Prambanan (BYP) region as a case study. The study used an Analytical Hierarchial Process in a Strengths Weaknesses Opportunities and Threats (SWOT-AHP) analysis to prioritize factors through pairwise comparisons and develop strategic alternatives based on expert perspectives. Primary data were collected through aSWOT-AHP questionnaire that was provided to 22 experts in tourism and disaster management in Central Java and Yogyakarta Special Provinces. Secondary data were obtained from government documents, specifically the Disaster Risk Reduction Plan for the BYP regions (2023–2027). The results indicate that “implementing land-use policies to promote tourism development while considering disasters” is the most important strategy, based on expert judgment. The BYP tourist destinations provided a case study to identify strategies to reduce disaster risk in tourism areas.

Implementing a remote self-screening programme for atrial fibrillation using digital health technology among community-dwellers aged 75 years and older: a qualitative evaluation

ObjectiveTo examine the feasibility of implementing remote atrial fibrillation (AF) self-screening among older people supported by a remote central monitoring system.DesignProcess evaluation of the Mass AF randomised clinical trial (ACTRN12621000184875)...

Coral reefs of Pakistan: a comprehensive review of anthropogenic threats, climate change, and conservation status

The coral reefs along Pakistan’s coastline are of ecological and economic significance. However, they are increasingly threatened by anthropogenic threats and climate change. These vulnerable ecosystems are declining due to various factors such as sedimentation, climate change, overfishing, destructive fishing practices, marine pollution, and tourism development. We found that 29%, 24%, 26%, 16% and 18% of the studies exceeding, marine pollution, overfishing/destructive fishing, coastal tourism, climate change and sedimentation, respectively; thus, indicating inadequate water quality status in part of Pakistan coastal water. These influences lead to several negative impacts, such as jeopardized coral health, decline in biodiversity, and the simplification of reef structures. In response to these threats, conservation efforts are imperative. This literature review provides an in-depth analysis of anthropogenic threats, climate change and the conservation of coral reefs in Pakistan. This review provides suggestions on how the country could better conserve its coral reef ecosystem. These include (1) initiatives such as establishing marine protected areas (MPAs), (2) encouraging sustainable fishing practices and reducing pollution, (3) developing the country as an ecotourism destination and implementing climate change adaptation measures, and (4) community engagement through awareness campaigns and fostering collaboration among, governmental organizations, non-governmental organizations (NGOs) and scientists. These comprehensive conservation policies address human-caused and other challenges, safeguarding their ecological, economic, and cultural relevance for future generations.

Cybersecurity in Electric and Flying Vehicles: Threats, Challenges, AI Solutions & Future Directions

Electric and Flying Vehicles (EnFVs) represent a transformative shift in transportation, promising enhanced efficiency and reduced environmental impact. However, their integration into interconnected digital ecosystems poses significant cybersecurity challenges, including cyber-physical threats, privacy vulnerabilities, and supply chain risks. This paper comprehensively explores these challenges and investigates artificial intelligence (AI)-driven solutions to bolster EnFV cybersecurity. The study begins with an overview of EnFV cybersecurity issues, emphasizing the increasing complexity of threats in digital transportation systems. Methodologically, the paper reviews existing literature to identify gaps and assesses recent advancements in AI for cybersecurity applications. Key methodologies include AI-powered intrusion detection, threat analysis leveraging machine learning algorithms, predictive maintenance strategies, and enhanced authentication protocols. Results underscore the effectiveness of AI technologies in mitigating EnFV cybersecurity risks, demonstrating improved threat detection and response capabilities. The study concludes by outlining future research directions, highlighting the need for continued innovation in AI, quantum computing resilience, blockchain applications, and ethical considerations. These findings contribute to a clearer understanding of EnFV cybersecurity dynamics and provide a roadmap for enhancing the security and reliability of future transportation systems.

P-TIMA: a framework of T witter threat intelligence mining and analysis based on a prompt-learning NER model

Abstract Open-source information platforms such as Twitter continuously provide the latest threat intelligence, including new vulnerabilities and in-the-wild exploitations of advanced persistent threat (APT) groups. Automated extraction of threat intelligence from Twitter has become crucial for defenders to access up-to-date threat knowledge. However, existing studies mainly rely on supervised learning methods to extract threat intelligence knowledge, such as entities, which require a large amount of annotated data. This paper presents Threat Intelligence Mining and Analysis based on Prompt Learning (P-TIMA), a framework specifically crafted for extracting and analyzing threat intelligence from Twitter. P-TIMA employs our innovative few-shot entity recognition method, SecEntPrompt (SEP), built on prompt learning, to extract vulnerability intelligence from Twitter. Additionally, P-TIMA analyzes and profiles the overarching vulnerability intelligence obtained from Twitter, along with in-the-wild exploitation intelligence of APT groups. The SEP improves the average entity recognition F1 score by 3.62-4.40 compared with the best-performing comparison model and outperforms the method based on the large language model on recognition performance and inference time. To validate our framework, we apply P-TIMA to extract vulnerability-related threat intelligence from real Twitter data. Through case studies, we then analyze trends in vulnerability threats and the exploitation capabilities of APT groups. In conclusion, our framework provides a more efficient and accurate method for extracting threat intelligence from Twitter, enabling defenders to stay up-to-date with the latest threat trends and helping them improve their defense strategies against cyber attacks.

Is Strengths, Weaknesses, Opportunities, and Threats Analysis Effective in Preventing and Controlling Coronavirus Disease-19? A qualitative study

Background: Coronavirus disease-19 (COVID-19) management requires continuous monitoring and evaluation based on various methods and models. This study aimed to evaluate the effectiveness of the strengths, weaknesses, opportunities, and threats (SWOT) analysis in preventing and controlling the COVID-19. Methods: This study was conducted using directed qualitative content analysis. A total of 15 participants, including 9 patients with COVID-19, a physician, a pharmacist, the head of the department of infectious diseases, a hospital manager, a nurse, and the head of a health center were selected through purposive sampling from May to July 2020. Data were collected using semi-structured interviews, each lasting 45 to 60 minutes and analyzed using Graneheim and Lundman’s qualitative content analysis method. Results: The analysis of the data revealed four main categories including the strengths (use of various media, supporting ideals), weaknesses) low understanding of disease risk, lack of vaccines, lack of appropriate tele-education and tele-medicine programs, infrastructural constraints), opportunities (people’s participation, family support, compliance with regulations), and threats (sanctions and poor control, the conflict between doubt and certainty, hidden layers of the disease, corona and social stigma). The internal and external analysis was determined as the main theme. Conclusion: The results suggest that it is necessary to make optimal use of the strengths and opportunities of the COVID-19 crisis and to develop participation and empathy among people in addition to complying with laws and making appropriate use of educational media. In this regard, the strengthening of tele-education infrastructure and transparency in presenting reports and methods for the prevention and control of the COVID-19 can be effective.

Impact of Technological Anxiety and Alien Invasion in H.G. Wells's The War of the Worlds.

The term science fiction is applied to those narratives in which unlike in pure fantasy an explicit attempt is made to render plausible the fictional world by reference to known, or imagined scientific principles, or to a projected advance in technology, or to a drastic change in the organization of society A Glossary of Literary Terms, Pg. 355. The science fiction has always a very important thematic topic about Alien invasion and their repercussions. Alien has always plundered or made humans slave while invading Earth in most of the books and movies. The book The War of the World’s speculates on this particular theme of Alien invasion on Earth by H. G. Wells. In the book The War of the Worlds a meteor lands on Earth in Horsell Common near to narrator’s house which later turns out to be an artificial cylinder launched towards Earth having Aliens in them. They invade earth to plunder the resources and to reign on Earth. The research paper will work on the title The Impact of Technological Anxiety and Alien Invasion in H.G. Wells's The War of the Worlds while looking for sub themes like, Technological Anxiety in the 19th Century, alien technologies, Comparative Analysis of Technological and Extraterrestrial Threats, and human adaptation. At the end the research paper we will see how Wells’s narrative ultimately reflects the broader anxieties of his time, revealing how technological progress and the unknown can together reshape our life.

Artificial Intelligence in Cancer: A SWOT Analysis

Cancer, a collection of maladies that has undergone extensive examination over centuries, remains a formidable challenge. Despite the array of available pharmacological and therapeutic interventions, the intricate molecular dynamics and heterogeneity of cancer continue to challenge the scientific community. Artificial Intelligence (AI) emerges as a promising avenue, offering the potential for expedited, precise diagnostics devoid of human expertise. Additionally, AI facilitates the tailoring of patient-specific therapeutic strategies targeting various facets of cancer, spanning macroscopic to microscopic levels. Nonetheless, it is imperative to scrutinize the potential benefits and limitations of AI technologies in this context. This review undertakes a comprehensive Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of AI's application in cancer. An extensive compilation of AI applications encompasses predictive modeling, diagnostic capabilities, prognostic assessments, and personalized therapeutic modalities, spanning genomic analyses to individualized treatment regimens. The synthesis of evidence suggests that the advantages of AI outweigh its drawbacks; nevertheless, obstacles to its widespread integration persist.

TTPXHunter: Actionable Threat Intelligence Extraction as TTPs from Finished Cyber Threat Reports

Understanding the modus operandi of adversaries aids organizations to employ efficient defensive strategies and share intelligence in the community. This knowledge is often present in unstructured natural language text within threat analysis reports. A translation tool is needed to interpret the modus operandi explained in the sentences of the threat report and convert it into a structured format. This research introduces a methodology named TTPXHunter for automated extraction of threat intelligence in terms of Tactics, Techniques, and Procedures (TTPs) from finished cyber threat reports. It leverages cyber domain-specific state-of-the-art natural language model to augment sentences for minority class TTPs and refine pinpointing the TTPs in threat analysis reports significantly. We create two datasets: an augmented sentence-TTP dataset of \(39,296\) sentence samples and a \(149\) real-world cyber threat intelligence report-to-TTP dataset. Further, we evaluate TTPXHunter on the augmented sentence and report datasets. The TTPXHunter achieves the highest performance of \(92.42\%\) f1-score on the augmented dataset, and it also outperforms existing state-of-the-art TTP extraction method by achieving an f1-score of \(97.09\%\) when evaluated over the report dataset. TTPXHunter significantly improves cybersecurity threat intelligence by offering quick, actionable insights into attacker behaviors. This advancement automates threat intelligence analysis and provides a crucial tool for cybersecurity professionals to combat cyber threats.

Vulnerability indicators of seawater intrusion in offshore aquifers

This study represents the first attempt to define vulnerability indicators for offshore fresh groundwater, extending prior analyses of the key threats to onshore coastal aquifers. The method applies an existing steady-state sharp-interface coastal aquifer model with semi-confined offshore extension to characterise the sensitivities of the tip and toe locations (top and bottom of the freshwater–seawater interface, respectively) and the freshwater and seawater volumes to environmental changes that threaten offshore freshwater resources. Sensitivity analysis applied to seven case studies quantifies their vulnerability to seawater intrusion from sea level rise, recharge change and a regional change in the onshore groundwater heads. The analysis demonstrates that the tip-to-toe distance in offshore aquifers is constant for different fresh groundwater discharge rates (under certain conditions). Otherwise, the interface is usually longer (flatter slope) in offshore aquifers than in onshore aquifers, except where the offshore limit of the aquifer is reached. The offshore extension of the aquifer leads to more seaward toe positions, increases the interface length and reduces the onshore seawater volume. As the freshwater discharge increases, the offshore freshwater volume becomes more vulnerable to impacts from changes in the freshwater discharge until the tip reaches the offshore limit or the toe crosses the coastline. For high values of freshwater discharge, the volume of freshwater stored offshore is more vulnerable to losses from changes in the freshwater discharge rate than is the onshore freshwater storage. For lower values of freshwater discharge, however, offshore freshwater storage is generally less vulnerable than onshore freshwater storage. Contrasts in the behaviour of onshore and offshore freshwater require that both need to be considered in coastal aquifer vulnerability assessments.

Live Memory Forensic: Capture and Analyzing Volatile Data

As almost 90% of malware is resident in memory, live memory forensics is an essential part of cybersecurity. Live memory forensics refers to the process of analyzing computer RAM or volatile memory (which is lost after rebooting) while the computer is running. This article provides depth on live memory forensics, which is key in the detection and analysis of cyber threats and forensics investigations. Case studies and actual events demonstrate how this method can detect unauthorized access and discover hidden malware, which will help law enforcement investigators. The practical uses of Live Memory Forensics are illustrated using real world examples. Currently, live memory forensics are faced with the temporal nature of volatile data, other technical challenges, the instantiation of indirect data related to data privacy, and evidence handling problems. The paper emphasizes the importance of moral attitude and careful handling of data to keep the forensic process incorrupt. Investigators and cybersecurity professionals should now have a good understanding of live memory forensics and how to utilize live memory forensics to enhance security across borders, not just as a detective technique.

Security in the era of wireless innovations: analysis of potential threats and protective measures

In today's interconnected world, wireless data transmission technologies have seamlessly integrated into the fabric of modern business operations. As reliance on these technologies grows, so does the imperative to ensure robust cyber security measures. Particularly in the age of wireless innovations, exemplified by the proliferation of the Internet of Things (IoT), the discourse surrounding the security of wireless technologies underscores the necessity of comprehending both established threats and the continuous emergence of new vulnerabilities. This underscores the urgent need for timely detection and mitigation strategies. While the convenience afforded by wireless data transmission technologies grants society unprecedented access to information and facilitates the management of diverse devices, processes, and systems, it also exposes users and modern information and communication systems (ICS) to significant cyber threats and vulnerabilities. Consequently, there arises a pressing need to address these challenges comprehensively. This research dissects contemporary methodologies aimed at restricting access to wireless networks, identifying potential vulnerabilities, and crafting effective responses to cyberattacks. It delves into various facets of cyber security, including data encryption, user authentication mechanisms, traffic monitoring protocols, and anomaly detection algorithms. Furthermore, it delves into the crucial aspect of educating personnel on wireless security practices, equipping them with threat awareness and incident response capabilities. Given the dynamic landscape of cybersecurity technologies and threats, this work seeks to establish a foundational understanding of the security landscape within wireless networks. By doing so, it aims to outline pragmatic strategies for effectively managing security risks, thereby fortifying the resilience of modern organizations and safeguarding critical information assets.

Cybersecurity Threats in E-Commerce: Trends and Mitigation Strategies

In the rapidly evolving digital landscape, e-commerce platforms have become prime targets for cyber threats, posing significant risks to businesses and consumers alike. This paper investigates the prevalent cybersecurity threats facing e-commerce and explores the current trends in these threats. The study identifies key threat categories such as phishing attacks, malware, data breaches, and insider threats, each of which has increasingly sophisticated and destructive capabilities. The paper further delves into the emerging trends in cybersecurity threats, including the use of artificial intelligence and machine learning by cybercriminals, vulnerabilities introduced by Internet of Things (IoT) devices, and the impact of regulatory changes on threat dynamics. Through an analysis of recent high-profile incidents and industry-specific threats, the study provides a comprehensive understanding of how these threats affect different sectors within e-commerce.In response to the growing threat landscape, the paper outlines various mitigation strategies. Technical measures such as encryption, secure payment gateways, and intrusion detection systems are discussed, alongside organizational practices including employee training, incident response planning, and access control policies. “The importance of legal and regulatory compliance is emphasized, highlighting the need for businesses to adhere to regulations like GDPR and CCPA.

Plug-and-play four-state modulation continuous-variable quantum key distribution

In this paper, a novel and highly efficient secure communication scheme is proposed through the integration of four-state modulation quantum key distribution and plug-and-play technology. This scheme serves the purpose of transmitting information reliably while preserving data confidentiality between two communicating parties. The most important advantage of the proposed continuous-variable QKD system is that the birefringence effect in fiber can be compensated automatically. Notably, simulation experimental evaluations are conducted, unveiling the performance of the plug-and-play four-state QKD which is as good as the four-state protocol when having small Rayleigh scattering noise. Furthermore, we conduct a comprehensive analysis of potential security threats targeted at the proposed protocol, thereby substantiating its robustness against such attacks. Moreover, theoretical justifications are provided to validate the security of the proposed scheme.

Integrating machine learning for sustaining cybersecurity in digital banks

Cybersecurity continues to be an important concern for financial institutions given the technology's rapid development and increasing adoption of digital services. Effective safety measures must be adopted to safeguard sensitive financial data and protect clients from potential harm due to the rise in cyber threats that target digital organizations. The aim of this study is to investigates how machine learning algorithms are integrated into cyber security measures in the context of digital banking and its benefits and drawbacks. We initially provide a general overview of digital banks and the particular security concerns that differentiate them from conventional banks. Then, we explore the value of machine learning in strengthening cybersecurity defenses. We revealed that insider threats, distributed denial of service (DDoS) assaults, ransomware, phishing attacks, and social engineering are main cyberthreats that are digital banks exposed. We identify the appropriate machine learning algorithms such as support vector machines (SVM), recurrent neural networks (RNN), hidden markov models (HMM), and local outlier factor (LOF) that are used for detection and prevention cyberthreats. In addition, we provide a model that considers ethical concerns while constructing a cybersecurity framework to address potential vulnerabilities in digital banking systems. The advantages and disadvantages of incorporating machine learning into the cybersecurity strategy of digital banks are outlined using strengths, weaknesses, opportunities, threats (SWOT) analysis. This study seeks to provide a thorough knowledge of how machine learning may strengthen cybersecurity procedures, protect digital banks, and maintain customer trust in the ecosystem of digital banking.

Leveraging Machine Learning for Proactive Threat Analysis in Cybersecurity

Leveraging Machine Learning for Proactive Threat Analysis in Cybersecurity

Securing data and preserving privacy in cloud IoT-based technologies an analysis of assessing threats and developing effective safeguard

The Internet of Things (IoT) is a powerful technology adopted in various industries. Applications of the IoT aim to enhance automation, productivity, and user comfort in a cloud and distributive computing environment. Cloud computing automatically stores and analyzes the large amounts of data generated by IoT-based applications. Cloud computing has become a crucial component of the information age through easier data administration and storage. Currently, government agencies, commercial enterprises, and end users are rapidly migrating their data to cloud environments. This may require end-user authentication, greater safety, and data recovery in the event of an attack. A few issues were discovered by authors after analysis and assessments of various aspects of the published research papers. The research analysis reveals that the existing methods need to be further improved to address the contemporary dangers related to data security and privacy. Based on the research reports, it can be stated that safe end-to-end data transmission in a cloud-IoT environment requires modifications and advancements in the design of reliable protocols. Upcoming technologies like blockchain, machine learning, fog, and edge computing mitigate data over the cloud to some level. The study provides a thorough analysis of security threats including their categorization, and potential countermeasures to safeguard our cloud-IoT data. Additionally, the authors have summarized cutting-edge approaches like machine learning and blockchain technologies being used in the data security privacy areas. Further, this paper discusses the existing problems related to data privacy and security in the cloud-IoT environment in today’s world and their possible future directions.