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Journal articles on the topic 'Natural threat'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Cartin-Ceba, Rodrigo, Lee B. Lu, and Anna Kolpakchi. "A ‘Natural’ Threat." American Journal of Medicine 120, no. 11 (2007): e3-e4. http://dx.doi.org/10.1016/j.amjmed.2007.04.018.

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Gebbeken, Norbert, and Theodor Krauthammer. "Natural Threat - Tornadoes." Bautechnik 93, no. 4 (2016): 243–53. http://dx.doi.org/10.1002/bate.201600001.

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Kuruvamanikindi Venkatesh, M Sai Kumar, Shaik Mohammed Maaz, Surekari Yashwanth Teja, and Dr. K. Pavan Kumar. "Automated Cyber Threat Identification and Natural Language Processing." International Journal of Scientific Research in Science, Engineering and Technology 12, no. 3 (2025): 89–97. https://doi.org/10.32628/ijsrset2512317.

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The time window between the disclosure of a new cyber vulnerability and its use by Cyber criminals has been getting smaller and smaller over time. Recent episodes, such asLog4j vulnerability, exemplifies this well. Within hours after the exploit being released, attackers started scanning the internet looking for vulnerable hosts to deploy threats like crypto currency miners and ransom ware on vulnerable systems.Thus,it becomes imperative for the cyber security defense strategy to detect threats and their capabilities as early as possible to maximize the success of prevention actions.Althoughcr

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Turner, Marian. "Hungarian natural history under threat." Nature 474, no. 7350 (2011): 139. http://dx.doi.org/10.1038/474139a.

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Shah, Anki, J. Kathyayani, D. Janani, E. Abhinav, and Rajashree Sutrawe. "CYBER THREAT DETECTION AND PROFILING USING AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–6. https://doi.org/10.55041/ijsrem.ncft025.

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The increasing reliance on the internet has escalated the frequency and sophistication of cyber threats, making timely identification and mitigation essential. This research presents an AI-powered framework for cyber threat detection and profiling using Natural Language Processing (NLP) and Machine Learning (ML) techniques. By utilizing Twitter as an Open Source Intelligence (OSINT) platform, the system collects real-time threat intelligence, classifies threats, and maps them to the MITRE ATT&CK framework to provide actionable insights. Key processes include data preprocessing, feature ext

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P. Karthik and B. Harish Kumar Reddy. "INTELLIGENT PROFILING OF EMERGING CYBER THREATS USING NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING." International Journal of Engineering Research and Science & Technology 21, no. 2 (2025): 986–96. https://doi.org/10.62643/ijerst.2025.v21.i2.pp986-996.

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The time window between the disclosure of a new cyber vulnerability and its use by cybercriminals has been getting smaller and smaller over time. Recent episodes, such as Log4j vulnerability, exemplifies this well. Within hours after the exploit being released, attackers started scanning the internet looking for vulnerable hosts to deploy threats like cryptocurrency miners and ransomware on vulnerable systems. Thus, it becomes imperative for the cybersecurity defense strategy to detect threats and their capabilities as early as possible to maximize the success of prevention actions. Although c

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Pasupuleti, Murali Krishna. "Threat Intelligence Automation Using Natural Language Processing on Dark Web Data." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 06 (2025): 399–411. https://doi.org/10.62311/nesx/rphcrcscrcp3.

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Abstract: This study presents an automated framework for threat intelligence gathering using Natural Language Processing (NLP) on dark web data. The growing sophistication of cyberattacks necessitates real-time detection of emerging threats. Traditional manual analysis of dark web forums is time-consuming and insufficient. This research proposes a hybrid NLP pipeline that integrates named entity recognition, sentiment analysis, and topic modeling to extract actionable threat indicators from darknet discussions. A dataset comprising over 100,000 dark web posts was analyzed, yielding high accura

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Raghav, Mayank. "NATURAL LANGUAGE PROCESSING BASED THREAT ASSESSMENT." International Journal of Engineering Applied Sciences and Technology 8, no. 6 (2023): 94–99. http://dx.doi.org/10.33564/ijeast.2023.v08i06.012.

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Military intelligence agencies are continually faced with the formidable task of evaluating and mitigating diverse risks in an increasingly complex and dynamic global landscape. NLP has emerged as a powerful tool for enhancing the capabilities of Military Intelligence operations. NLP techniques enable the automatic extraction, analysis, and interpretation of information from vast volumes of unstructured textual data, such as open-source intelligence reports, social media, intercepted communications. By processing and understanding these textual sources, NLP systems can assist Military Intellig

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Tohir, RIzki Kurnia, and Fadlan Pramatana. "PEMETAAN ANCAMAN DAN KARAKTERISTIK KEBAKARAN HUTAN DAN LAHAN (KARHUTLA) PROVINSI LAMPUNG." Wahana Forestra: Jurnal Kehutanan 15, no. 2 (2020): 12–27. http://dx.doi.org/10.31849/forestra.v15i2.4705.

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Lampung Province has the threat of Forest and Land Fires (FLF) based on incident reports. There is a lack of data on how the threat level of the forest and land fires, so this research is important to do. This study aims to analyze the track record and potential for FLF incidents, to analyze the characteristics and level of the FLF threat. Threat mapping is done by weighting and scoring 11 variables. These variables are divided into natural factors and human factors. The results showed that the equation that gives a weighting of 90% to natural factors. The characteristics of FLF show that natu

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Czarny, Elżbieta, Jerzy Menkes, and Patryk Toporowski. "Gazprom - threat to Europe?" Economics and Business Review 9, no. 1 (2009): 44–66. http://dx.doi.org/10.18559/ebr.2009.1.551.

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The dominant part of production and dispatch of natural gas goes through Gazprom, a company controlled by the state authorities of Russia. We analyze the threats resulting from the strengthening economic and political position of Gazprom. We analyze the most important symptoms of Gazprom&apos;s increasing market power (international price discrimination, elimination of competitors, overtaking companies on the related markets) making it a natural monopolist on the Europe an market of natural gas and its dispatch. We analyze Gazprom&apos;s ownership structure demonstrating increasing int

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Sangita, Agarwal*1 Abhishek Saha2 Sufia Zaman2 &. Abhijit Mitra3. "RESPONDENT ANALYSIS OF THREATS TO BRACKISH WATER FISHERIES IN ODISHA." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 12 (2018): 203–9. https://doi.org/10.5281/zenodo.2525772.

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The present study is a first order analysis on the threats exerted on the fishery sector in the state of Odisha through the survey mode of public opinion. The major threats considered in the present research program are erosion, natural disasters, over exploitation of natural resources, pollution, siltation and sea level rise. The magnitude of each of these threats was evaluated by five categories of stakeholders (Policy makers, Researchers, Agriculturist, Fisherman and Local inhabitants). It is observed that the highest value of threat is assigned to natural disaster (848.4) followed by pollu

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Amjad Jumani, Amber Baig, Engr. Dr. Shamim Akhtar, Muhammad Shahmir Shamim, Hira Zaheer, and Areej Changaiz. "AUTOMATING CYBER THREAT INTELLIGENCE EXTRACTION USING NATURAL LANGUAGE PROCESSING TECHNIQUES." Kashf Journal of Multidisciplinary Research 2, no. 06 (2025): 184–201. https://doi.org/10.71146/kjmr498.

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The increasing negligence and complexity of online confrontations have made it abundantly clear that an organization must place a premium on real-time, ready-to-use, and expandable Cyber Threat Intelligence (CTI) strategies. The classical approach to CTI collection and analysis that heavily involves manual work over raw unstructured text-based data including threat reports, blogs, and advisories cannot keep up with the requirements of current cybersecurity threats. In this study, an intermediate form of Natural Language Processing (NLP) framework is introduced utilizing the state-of-the-art tr

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Jermy, A. C. "Plant invaders: The threat to natural ecosystems." Biological Conservation 79, no. 2-3 (1997): 313. http://dx.doi.org/10.1016/s0006-3207(97)89602-4.

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Cowling, Richard M., and David M. Richardson. "Plant invaders: The threat to natural ecosystems." Trends in Ecology & Evolution 10, no. 12 (1995): 508–9. http://dx.doi.org/10.1016/s0169-5347(00)89215-0.

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Zaborowska, Magdalena, Jadwiga Wyszkowska, Agata Borowik, and Jan Kucharski. "Bisphenols—A Threat to the Natural Environment." Materials 16, no. 19 (2023): 6500. http://dx.doi.org/10.3390/ma16196500.

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Negative public sentiment built up around bisphenol A (BPA) follows growing awareness of the frequency of this chemical compound in the environment. The increase in air, water, and soil contamination by BPA has also generated the need to replace it with less toxic analogs, such as Bisphenol F (BPF) and Bisphenol S (BPS). However, due to the structural similarity of BPF and BPS to BPA, questions arise about the safety of their usage. The toxicity of BPA, BPF, and BPS towards humans and animals has been fairly well understood. The biodegradability potential of microorganisms towards each of thes

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Waisová, Šárka. "From Threat to Opportunity." Czech Journal of International Relations 48, no. 3 (2013): 32–61. http://dx.doi.org/10.32422/cjir.318.

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The research of the relationship between conflicts and natural resourceshas been dominated by an understanding of natural resources as a causeof conflicts, and the term “conflict resources” has been used in connectionwith this. Here I o!er another approach and present the hypothesis that alack of environmental resources (environmental resources and rawmaterials are together understood as natural resources) and environmentaldegradation can o!er a peace-building opportunity. I argue thatenvironmental resources are not transportable, extractable or lootable,which decreases their market significan

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Emrich, Christopher T., Yao Zhou, Sanam K. Aksha, and Herbert E. Longenecker. "Creating a Nationwide Composite Hazard Index Using Empirically Based Threat Assessment Approaches Applied to Open Geospatial Data." Sustainability 14, no. 5 (2022): 2685. http://dx.doi.org/10.3390/su14052685.

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The US is exposed to myriad natural hazards causing USD billions in damages and thousands of fatalities each year. Significant population and economic growth during the last several decades have resulted in more people residing in hazardous places. However, consistent national-scale hazard threat assessment techniques reflecting the state of hazard knowledge are not readily available for application in risk and vulnerability assessments. Mapping natural hazard threats is the crucial first step in identifying and implementing threat reduction or mitigation strategies. In this study, we demonstr

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Datta, N. Bala Suresh. "Holistic Cyber Threat Intelligence System with Bert for Advanced Threat Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2662–64. https://doi.org/10.22214/ijraset.2025.68780.

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Cyber threats are evolving at an unprecedented rate, making traditional security measures insufficient in detecting and mitigating sophisticated attacks. This project introduces an AI-powered Cyber Threat Intelligence System that leverages machine learning, natural language processing (NLP), and automated threat analysis to enhance cybersecurity defenses. The system integrates data from multiple threat intelligence sources, such as OSINT feeds, security reports, and real-time network traffic, to identify, classify, and prioritize security threats. By employing a BERT-based NLP engine, the syst

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Bhaskar, Parumanchala, Farooq Sunar Mahammad, K. Ramachari, et al. "Automated Cyber Threat Identification Using Natural Language Processing." International Research Journal of Innovations in Engineering and Technology 09, Special Issue (2025): 395–99. https://doi.org/10.47001/irjiet/2025.inspire64.

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This abstract challenge by leveraging Natural Language Processing (NLP) to automate cyber threat identification. The proposed system utilizes advanced NLP techniques to analyse vast amounts of textual data from sources such as cybersecurity reports, social media, forums, and dark web communications. The proliferation of cyberthreats in today's digital world poses serious security and privacy issues. Because malevolent behaviour is dynamic, traditional threat detection techniques usually fall behind. By increasing threat detection's precision, effectiveness, and scalability, the solution seeks

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Muhammed, Khaleel, Aavudai Anandhi, Gang Chen, and Kevin Poole. "Define–Investigate–Estimate–Map (DIEM) Framework for Modeling Habitat Threats." Sustainability 13, no. 20 (2021): 11259. http://dx.doi.org/10.3390/su132011259.

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As the human population increases, the landscape is altered to provide housing, food, and industry. Human activity poses a risk to the health of natural habitats that, in turn, affect biodiversity. Biodiversity is necessary for a functioning ecosystem, as species work synergistically to create a livable environment. It is, therefore, important to know how human practices and natural events threaten these habitats and the species living in them. A universal method of modeling habitat threats does not exist. This paper details the use of a literature review to formulate a new framework called De

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Muhammed, Khaleel, Aavudai Anandhi, Gang Chen, and Kevin Poole. "Define–Investigate–Estimate–Map (DIEM) Framework for Modeling Habitat Threats." Sustainability 13, no. 20 (2021): 11259. http://dx.doi.org/10.3390/su132011259.

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As the human population increases, the landscape is altered to provide housing, food, and industry. Human activity poses a risk to the health of natural habitats that, in turn, affect biodiversity. Biodiversity is necessary for a functioning ecosystem, as species work synergistically to create a livable environment. It is, therefore, important to know how human practices and natural events threaten these habitats and the species living in them. A universal method of modeling habitat threats does not exist. This paper details the use of a literature review to formulate a new framework called De

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Arslan, Dilara, Kerim Çiçek, Ömer Döndüren, and Lisa Ernoul. "Threat Ranking to Improve Conservation Planning: An Example from the Gediz Delta, Turkey." Land 10, no. 12 (2021): 1381. http://dx.doi.org/10.3390/land10121381.

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Mediterranean wetlands are among the most threatened natural areas. The needs and demands of an increasing human population are modifying land use and converting natural habitats into artificial areas. In order to combat these trends, effective conservation planning needs to provide clear, systematic identification of threats to find sustainable conservation strategies. In this case study, we evaluated current threats in the Gediz Delta (Turkey) using a multi-method approach. First, we did a comprehensive literature review and stakeholder interviews to identify existing threats. We then did a

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23

Abhijit, Mitra. "Threat Detection Technique in Context to Mangrove Ecosystem of Indian Sundarbans." Annals of Marine Science 1, no. 1 (2017): 028–31. https://doi.org/10.17352/ams.000005.

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The mangrove ecosystem in the lower Gangetic delta is one of the most biologically productive and taxonmically diverse ecosystems of the tropics, which is threatened by a variety of stressors. The main threats operating in this fragile ecosystem are erosion, natural disasters (like cyclone, wave actions, tidal surges etc), over exploitation of natural resources, pollution, siltation, sea level rise etc. In this research, we have attempted to develop a mechanism to evaluate and rank the magnitude of threats based through collection of expert opinion from different groups of respondents. The res

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Manasa, K., P. S. S. Neeraj, V. V. V. Sainadh, G. Gayatri Priya, and V. P. V. Bharathi. "Adaptive Cyber Attack Prediction Using Multi-Source Data." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43159.

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Cyber threats are evolving rapidly, making traditional detection systems inadequate for identifying sophisticated attack patterns. This paper presents an adaptive deep learning approach for cyber threat detection using multi-source textual data. The proposed system leverages Long Short-Term Memory (LSTM) networks to analyze and classify cyber threat intelligence into multiple categories, including phishing, malware, ransomware, and SQL injection. Advanced Natural Language Processing (NLP) techniques, such as lemmatization, stopword removal, and text normalization, enhance the model's ability t

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Edim Bassey Edim, Akpan Itoro Udofot, and Omotosho Moses Oluseyi. "AI-augmented cyber security threat intelligence – enhancing situational awareness." International Journal of Science and Research Archive 14, no. 1 (2025): 890–97. https://doi.org/10.30574/ijsra.2025.14.1.2650.

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In the evolving landscape of cyber threats, traditional threat intelligence methods are increasingly inadequate for addressing the complexity and speed of modern attacks. This paper explores the transformative impact of Artificial Intelligence (AI) on enhancing cyber security threat intelligence and situational awareness. By leveraging advanced AI technologies—such as machine learning, natural language processing, and data analytics—organizations can significantly improve their ability to detect, analyze, and respond to threats. We provide a comprehensive review of current AI applications in t

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Srija, E., G. S. V. Mithelesh, G. Manas Reddy,, and MD Irfan. "Blockchain-Based Social Networking Model Empowered by Non-Fungible Tokens." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–7. https://doi.org/10.55041/ijsrem.ncft023.

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The increasing reliance on the internet has escalated the frequency and sophistication of cyber threats, making timely identification and mitigation essential. This research presents an AI-powered framework for cyber threat detection and profiling using Natural Language Processing (NLP) and Machine Learning (ML) techniques. By utilizing Twitter as an Open Source Intelligence (OSINT) platform, the system collects real-time threat intelligence, classifies threats, and maps them to the MITRE ATT&CK framework to provide actionable insights. Key processes include data preprocessing, feature ext

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Vynograd, Nataliya. "Natural Foci Diseases as a Stable Biological Threat." Archivum Immunologiae et Therapiae Experimentalis 62, no. 6 (2014): 445–47. http://dx.doi.org/10.1007/s00005-014-0316-8.

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Hutasoit, SE., MSi., MSE, Posma Sariguna Johnson Kennedy. "Natural Balance of Power, The Balance of Power Between Countries that Promote Economic." Fundamental Management Journal 1, no. 01 (2017): 58–71. http://dx.doi.org/10.33541/fjm.v1i01.195.

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Traditionally, external threat comes from the asymmetrical military power among countries, where the concept has now evolved into a natural balance of power. In this paper, the analysis of natural balance of power will be done through a literature review. Today, modern threats are always connected to the military force that is proportional to economy of a country. The level of militarization, which is too high and out of proportion with the ability of economies, perceived as a threat to countries in the vicinity. Instead strength in the economy with healthy competition is expected as a nationa

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Yadav, Dr Neha. "AI in Cybersecurity: A Literature review." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03378.

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Abstract - In cybersecurity, artificial intelligence is revolutionizing incident response, risk management, and threat identification in a progressively hostile cyber threat landscape. This research presents a thorough literature review on AI in cybersecurity, focusing on both aspects of the balance sheet. This paper discusses how AI-driven technologies like machine learning, deep learning, natural language processing, and expert systems improve security frameworks through predictive analytics, real-time threat intelligence, and anomaly detection. The research explores various uses of AI such

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Kelvin Ovabor, Ismail Oluwatobiloba Sule-Odu, Travis Atkison, Adetutu Temitope Fabusoro, and Joseph Oluwaseun Benedict. "AI-driven threat intelligence for real-time cybersecurity: Frameworks, tools, and future directions." Open Access Research Journal of Science and Technology 12, no. 2 (2024): 040–48. http://dx.doi.org/10.53022/oarjst.2024.12.2.0135.

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AI-driven threat intelligence is transforming cybersecurity by enhancing real-time threat detection, analysis, and response capabilities. This paper reviews state-of-the-art AI frameworks, machine learning models, and tools that support threat intelligence, providing a survey of current research in the field and identifying challenges and future directions for real-time cybersecurity. Techniques such as supervised and unsupervised learning, reinforcement learning, and natural language processing (NLP) contribute to the robustness of threat detection, while evolving frameworks and ethics guide

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Ramasankar Molleti, Vinod Goje, Puneet Luthra, and Prathap Raghavan. "Automated threat detection and response using LLM agents." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 079–90. http://dx.doi.org/10.30574/wjarr.2024.24.2.3329.

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The increase of cyber threats from individual cases to a worldwide problem is the reason why people have shifted their cybersecurity perspectives. Basic defense processes, originally well understood and effective, fail to match modern attacks’ complexity and velocity. Taking into consideration LLMs as a recent addition to AI, this paper aims at discussing their application in integrating threat detection and response automation systems. As a result, LLMs, which have higher capabilities for natural language processing, deliver a revolutionary perspective regarding cybersecurity. Since LLM agent

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Ramasankar, Molleti, Goje Vinod, Luthra Puneet, and Raghavan Prathap. "Automated threat detection and response using LLM agents." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 079–90. https://doi.org/10.5281/zenodo.15067889.

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The increase of cyber threats from individual cases to a worldwide problem is the reason why people have shifted their cybersecurity perspectives. Basic defense processes, originally well understood and effective, fail to match modern attacks’ complexity and velocity. Taking into consideration LLMs as a recent addition to AI, this paper aims at discussing their application in integrating threat detection and response automation systems. As a result, LLMs, which have higher capabilities for natural language processing, deliver a revolutionary perspective regarding cybersecurity. Since LLM

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P.Ramya, P., R. R.Anitha, J. Rajalakshmi, and R. R.Dineshkumar. "Integrating Quantum Computing and NLP for Advanced Cyber Threat Detection." Journal of Cybersecurity and Information Management 14, no. 2 (2024): 186–97. http://dx.doi.org/10.54216/jcim.140213.

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The exponential growth of digital data and the increasing sophistication of cyber threats demand more advanced methods for threat analysis. This paper explores the integration of quantum computing and natural language processing (NLP) to enhance cyber threat analysis. Traditional computing methods struggle to keep up with the scale and complexity of modern cyber threats, but quantum computing offers a promising avenue for accelerated data processing, while NLP provides sophisticated tools for interpreting and understanding human language, crucial for analysing threat intelligence. Our proposed

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Dołęga, Marta, and Krzysztof Biernat. "Procesy zarządzania ryzykiem ekologicznym." Studia Ecologiae et Bioethicae 7, no. 1 (2009): 157–64. http://dx.doi.org/10.21697/seb.2009.7.1.10.

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Risk management relies on identification, estimation, control and finance of potential risks. Excient process of management against alternate of ecological threat it’s people and nature resources protection before threats related to pollution of water, air, soil and other components of environment, which balance has been broken by reason of pronouncement of unexpected natural phenomenon, industrial failure and human activities. Negative environmental results are caused in case of pronouncement of threat damage to environmental component pollutions and ecosystems changes.

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Kashyap, Gaurav. "AI for Threat Detection and Mitigation: Using AI to identify and respond to cybersecurity threats in real-time." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem10936.

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As the digital landscape evolves and cyber threats become increasingly sophisticated, traditional security systems struggle to keep up with the volume, variety, and velocity of attacks. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing cybersecurity by enabling the automated detection, analysis, and mitigation of threats in real-time. By leveraging machine learning (ML) algorithms, natural language processing (NLP), and anomaly detection, AI can process vast amounts of data, identify patterns, and respond to potential threats faster and more accurately than conventional

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Sun, Tianfang, Pin Yang, Mengming Li, and Shan Liao. "An Automatic Generation Approach of the Cyber Threat Intelligence Records Based on Multi-Source Information Fusion." Future Internet 13, no. 2 (2021): 40. http://dx.doi.org/10.3390/fi13020040.

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With the progressive deterioration of cyber threats, collecting cyber threat intelligence (CTI) from open-source threat intelligence publishing platforms (OSTIPs) can help information security personnel grasp public opinions with specific pertinence, handle emergency events, and even confront the advanced persistent threats. However, due to the explosive growth of information shared on multi-type OSTIPs, manually collecting the CTI has had low efficiency. Articles published on the OSTIPs are unstructured, leading to an imperative challenge to automatically gather CTI records only through natur

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Saptono, Edy, Ribka Keiza, and Joni Wijayanto. "Security management of national park security in the border of Indonesia - Malaysia." Technium Social Sciences Journal 23 (September 9, 2021): 508–18. http://dx.doi.org/10.47577/tssj.v23i1.4371.

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Boundary is an important part of the state's sovereignty and becomes a fortress of all kinds of threats. The threat about security of natural resources is one of non-military threat which affects Indonesia’s security. Betung Kerihun National Park adjoins to Malaysia in northern of Indonesia, so it can be vulnerable to threats. The purposes of this research are to analyze the threat of security toward management of Betung Kerihun National Park (TNBK), the efforts made to overcome the security threat, and the management of TNBK on the border of Indonesia and Malaysia. The research uses qualitati

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Mrozowska, Alicja. "CATALOG OF NON-MILITARY RISKS OCCURRING IN MARINE AREAS, WITH SPECIAL CONSIDERATION FOR THREATS GENERATED BY NATURAL CONDITIONS." Rocznik Bezpieczeństwa Morskiego XIV (November 2, 2020): 1–2. http://dx.doi.org/10.5604/01.3001.0014.6070.

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The study indicates that not only the state of an armed conflict which may pose a significant threat to people, and the natural environment, but also non-military events occurring during peace, even during the daily functioning of the state and its citizens. The considered problem is aimed at developing a catalogue of forecasted non-military threats, taking into account the development of already defined threats generated by the forces of nature. In addition, the paper specifies the determinants of the occurring natural hazards, and indicates the areas of par-ticular sensitivity, i.e. the prog

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Islam, S. A. Mohaiminul, MD Shadikul Bari, Ankur Sarkar, A. J. M. Obaidur Rahman Khan, and Rakesh Paul. "AI-Powered Threat Intelligence: Revolutionizing Cybersecurity with Proactive Risk Management for Critical Sectors." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 7, no. 01 (2024): 1–8. https://doi.org/10.60087/jaigs.v7i01.291.

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The rapid evolution of cyber threats has necessitated a paradigm shift in cybersecurity strategies, particularly in critical sectors such as healthcare, finance, energy, and transportation. This paper explores the transformative role of AI-powered threat intelligence in revolutionizing cybersecurity practices. By leveraging advanced machine learning algorithms, natural language processing, and predictive analytics, AI-driven systems can detect, analyze, and mitigate threats with unprecedented speed and accuracy. This research highlights the integration of real-time data processing, threat inte

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Stein, Dr Laura. "ADVANCING PROACTIVE CYBERSECURITY THROUGH CYBER THREAT INTELLIGENCE MINING: A COMPREHENSIVE REVIEW AND FUTURE DIRECTIONS." International Journal of Cyber Threat Intelligence and Secure Networking 2, no. 02 (2025): 1–7. https://doi.org/10.55640/ijctisn-v02i02-01.

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In an era of increasingly sophisticated cyber threats, proactive cybersecurity has become essential for defending digital infrastructures. Cyber Threat Intelligence (CTI) mining plays a pivotal role in anticipating, detecting, and mitigating potential attacks by analyzing structured and unstructured threat data. This paper presents a comprehensive review of existing approaches, tools, and frameworks in CTI mining, highlighting advancements in natural language processing, machine learning, and threat taxonomy extraction. The study categorizes key methodologies used to extract actionable insight

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VÁLEK, Dalibor, and Radomír ŠČUREK. "THREAT OF THE ELECTRICAL TRANSMISSION SYSTEMS BY THE NATURAL." Krízový Manažment 12, no. 1 (2013): 70–76. http://dx.doi.org/10.26552/krm.c.2013.1.70-76.

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Semenenko, O., Y. Dobrovolsky, I. Tselishchev, V. Ivanov, Т. Akinina, and V. Dubovyi. "ON DETERMINATION OF THREATS TO THE NATIONAL SECURITY OF UKRAINE FROM THE UNDERWATER SPACE." Collection of scientific works of Odesa Military Academy 2, no. 14 (2021): 187–92. http://dx.doi.org/10.37129/2313-7509.2020.14.2.187-192.

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The main factors determining the threats to Ukraine’s national security from the maritime direction in the part concerning the underwater space within the territorial sea of Ukraine are: increase of naval forces and their armament by other countries of the Black Sea region; presence of territorial claims; unresolved issues regarding the borders of economic maritime zones and the distribution of the continental shelf; economic interests of Ukraine and encroachment on its natural resources; conducting intelligence activities in national waters; terrorism at sea. Comprehensive consideration of th

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Munawwar, Arif, Jusni, and Aswan Andi. "The Development Strategy of Batu Papan Natural Bath Tourism Region in Palopo City." International Journal of Innovative Science and Research Technology 8, no. 2 (2023): 854–59. https://doi.org/10.5281/zenodo.7677965.

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The unoptimal development of Batu Papan Natural Bath Tourism Region in Palopo City has resulted in decreased tourist visits number. This study aims to identify and analyze the supporting factors and inhibiting factors and to formulate a development strategy for Batu Papan Natural Bath Tourism Region in Palopo City. This study is a descriptive study with mixed methods approach between quantitative and qualitative analysis. SWOT analysis is used to analyze the existing factors of strengths, weaknesses, opportunities, threats and formulate the right development strategy. The findings indicate tha

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Rohit Kumar Bisht. "Cybersecurity and Artificial Intelligence: How AI is Being Used in Cybersecurity to Improve Detection and Response to Cyber Threats." Tuijin Jishu/Journal of Propulsion Technology 45, no. 02 (2024): 6749–61. http://dx.doi.org/10.52783/tjjpt.v45.i02.7577.

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This comprehensive research paper explores the intersection of artificial intelligence (AI) and cybersecurity, focusing on how AI technologies are revolutionizing threat detection and response mechanisms. The study examines various AI applications in cybersecurity, including machine learning algorithms for anomaly detection, natural language processing for threat intelligence, and AI-driven automation in security operations. Through an analysis of current implementations, challenges, and future trends, this paper provides insights into the transformative impact of AI on cybersecurity practices

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Aguilar-Luzón, María del Carmen, Beatriz Carmona, and Ana Loureiro. "Future Actions towards Climate Change: The Role of Threat Perception and Emotions." European Journal of Sustainable Development 12, no. 4 (2023): 379. http://dx.doi.org/10.14207/ejsd.2023.v12n4p379.

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This work explores the emotional activation (anxiety, fear, and anger) resulting from the cognitive-emotional process related to the perception of climate change: a) when climate change is considered the result of human action (anthropogenic) and b) when climate change is considered the result of the planet’s natural activity. An experimental study was conducted with 104 participants exposed to two types of messages about the causes of climate change. The results showed that exposure to the anthropogenic influence led to greater perception of threat to humans. Moreover, the participants in the

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Shayma Sultanan, Md Mostafizur Rahman, Mohammad Shahadat Hossain, Md. Najmul Gony, and AL Rafy. "AI-powered threat detection in modern cybersecurity systems: Enhancing real-time response in enterprise environments." World Journal of Advanced Engineering Technology and Sciences 6, no. 2 (2022): 136–46. https://doi.org/10.30574/wjaets.2022.6.2.0079.

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The rising complexity of cyber threats demands enhanced security systems to protect computer networks. Traditional detection methods like signature-based systems cannot handle the sophisticated security attacks in the modern landscape. Artificial intelligence (AI) serves as a transforming technology that helps detect threats together with their real-time response mechanisms. The research evaluates AI technology as it improves cybersecurity elements by studying its deployment within enterprise security systems. Machine learning, deep learning, and natural language processing functions in AI tec

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Researcher. "THE ROLE OF AI IN DETECTING INSIDER THREATS IN HEALTHCARE ORGANIZATIONS." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 239–48. https://doi.org/10.5281/zenodo.13960643.

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This article explores the critical role of Artificial Intelligence (AI) in detecting and preventing insider threats within healthcare organizations. It examines the growing challenges of data security in the healthcare sector, highlighting the significant financial and reputational risks posed by insider threats. The article discusses various types of insider threats, including malicious actors, negligent employees, and compromised credentials. It then delves into how AI enhances insider threat detection through behavioral analytics, machine learning, natural language processing, and predictiv

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Sridevi Kakolu, Muhammad Ashraf Faheem, and Muhammad Aslam. "Integrating Natural Language Processing with Cybersecurity Protocols: Real-Time Analysis of Malicious Intent in Social Engineering Attack." International Journal of Science and Research Archive 1, no. 1 (2020): 082–95. http://dx.doi.org/10.30574/ijsra.2020.1.1.0020.

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Natural Language Processing (NLP) is a great way of supplementing cybersecurity. It has come into its own when fighting social engineering attacks, which feed on human weaknesses. We explore the possibilities of NLP when detecting malicious intent, using linguistic analysis to identify the threat (and mitigate the risk) in advance. With sentiment analysis, keyword detection, and contextual understanding, NLP can alert people about potentially harmful communications before a security incident. Including machine learning models makes NLP flexible enough to change with emerging attack patterns. T

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Vamsi Krishna Vemulapalli. "AI-driven cybersecurity: The future of adaptive threat defense." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 3248–55. https://doi.org/10.30574/wjarr.2025.26.2.1953.

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The rapid evolution of cyber threats has rendered traditional security approaches increasingly inadequate against sophisticated attackers. This article introduces an advanced AI-driven cybersecurity platform that leverages continuous learning and automated response capabilities to provide comprehensive protection across enterprise environments. The solution integrates multiple machine learning approaches—including behavioral analytics, deep learning models, and natural language processing—to establish baseline patterns, detect anomalies, and identify threats invisible to conventional tools. Th

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Fang, Yong, Jian Gao, Zhonglin Liu, and Cheng Huang. "Detecting Cyber Threat Event from Twitter Using IDCNN and BiLSTM." Applied Sciences 10, no. 17 (2020): 5922. http://dx.doi.org/10.3390/app10175922.

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In the context of increasing cyber threats and attacks, monitoring and analyzing network security incidents in a timely and effective way is the key to ensuring network infrastructure security. As one of the world’s most popular social media sites, users post all kinds of messages on Twitter, from daily life to global news and political strategy. It can aggregate a large number of network security-related events promptly and provide a source of information flow about cyber threats. In this paper, for detecting cyber threat events on Twitter, we present a multi-task learning approach based on t

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