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Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities

Mohamed Amine Ferrag, Fatima Alwahedi, Ammar Ayman Battah, Bilel Cherif et al. • Internet of Things and Cyber-Physical Systems (2025)

This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, s...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.iotcps.2025.01.001

Cybersecurity in Hospitals: A Systematic, Organizational Perspective

Mohammad S. Jalali, Jessica Kaiser • Journal of Medical Internet Research (2018)

BACKGROUND: Cybersecurity incidents are a growing threat to the health care industry in general and hospitals in particular. The health care industry has lagged behind other industries in protecting its main stakeholder (ie, patients), and ...

OPENALEX cc-by DOI: https://doi.org/10.2139/ssrn.3100364 PMC: 5996174

INTERNATIONAL CYBERSECURITY EXPERIENCE AND CYBERSECURITY MODELS

Zina Zinelovna Nurysh, E.I. Isibaeva, Zhanna B. Shayakhmetova, Darkan Serik • «Вестник Атырауского университета имени Халела Досмухамедова» (2026)

In the article, the authors explore the state's capabilities in cybersecurity issues directly related to foreign national security experience. The mechanisms of public administration will be considered in order to improve cybersecurity in t...

OPENALEX cc-by DOI: https://doi.org/10.47649/vau.26.v80.i1.54

Cybersecurity

Cláudia Canongia, Raphael Mandarino • IGI Global eBooks (2011)

This chapter introduces the theme of cybersecurity, its importance in the actual scenario, and the challenges of the new Information Society, whose critical development factors are the technological revolution and innovation. The revolution...

OPENALEX DOI: https://doi.org/10.4018/978-1-61350-168-9.ch009

Cybersecurity knowledge graphs

Leslie F. Sikos • Knowledge and Information Systems (2023)

Abstract Cybersecurity knowledge graphs, which represent cyber-knowledge with a graph-based data model, provide holistic approaches for processing massive volumes of complex cybersecurity data derived from diverse sources. They can assist s...

OPENALEX cc-by DOI: https://doi.org/10.1007/s10115-023-01860-3

Large Language Models in Cybersecurity: State-of-the-Art

Farzad Nourmohammadzadeh Motlagh, Mehrdad Hajizadeh, Mehryar Majd, Pejman Najafi et al. • International Conference on Information Systems Security and Privacy (ICISSP) (2025)

The rise of Large Language Models (LLMs) has revolutionized our comprehension of intelligence bringing us closer to Artificial Intelligence. Since their introduction, researchers have actively explored the applications of LLMs across divers...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.5220/0013377600003899

From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy

Maanak Gupta, Charankumar Akiri, Kshitiz Aryal, Eli Parker et al. • IEEE Access (2023)

Undoubtedly, the evolution of Generative AI (GenAI) models has been the highlight of digital transformation in the year 2022. As the different GenAI models like ChatGPT and Google Bard continue to foster their complexity and capability, it’...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1109/access.2023.3300381

Explainable artificial intelligence for cybersecurity: a literature survey

Fabien Charmet, Harry Chandra Tanuwidjaja, Solayman Ayoubi, Pierre-François Gimenez et al. • Annals of Telecommunications (2022)

Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essent...

OPENALEX cc-by DOI: https://doi.org/10.1007/s12243-022-00926-7

Cyberethics, Cybersafety, and Cybersecurity

Portia Pusey, William Anthony Sadera • Journal of Digital Learning in Teacher Education (2011)

In teacher education programs, preservice teachers learn about strategies to appropriately integrate computer-related and Internet-capable technologies into instructional settings to improve student learning. Many presume that preservice te...

OPENALEX DOI: https://doi.org/10.1080/21532974.2011.10784684

Computer Vision and Natural Language Processing

Peratham Wiriyathammabhum, Douglas A. Summers-Stay, Cornelia M. Fermuller, Yiannis Aloimonos • ACM Computing Surveys (2016)

Integrating computer vision and natural language processing is a novel interdisciplinary field that has received a lot of attention recently. In this survey, we provide a comprehensive introduction of the integration of computer vision and ...

OPENALEX DOI: https://doi.org/10.1145/3009906

THE ROLE OF NATURAL LANGUAGE PROCESSING

V Geetha, C.K. Gomathy, Mr.D.Sri Datta Vallab Yaratha Yagn, Sai Praneesh • INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (2023)

Natural Language Processing (NLP) is a rapidly evolving field in the intersection of computer science, artificial intelligence, and linguistics. This article provides an overview of NLP, tracing its historical development from early rule-ba...

OPENALEX Open Access DOI: https://doi.org/10.55041/ijsrem27094

Quantifying Uncertainties in Natural Language Processing Tasks

Yijun Xiao, William Yang Wang • Proceedings of the AAAI Conference on Artificial Intelligence (2019)

Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper,...

OPENALEX Open Access DOI: https://doi.org/10.1609/aaai.v33i01.33017322

Natural Language Processing

Xavier Vasques • Wiley eBooks (2024)

This book chapter focuses on natural language processing (NLP) and covers various applications such as classifying messages as spam or ham, sentiment analysis, and the use of Bidirectional Encoder Representations from Transformers (BERT). T...

OPENALEX DOI: https://doi.org/10.1002/9781394220649.ch4

Forecasting student achievement in MOOCs with natural language processing

Carly D. Robinson, Michael Yeomans, Justin Reich, Chris S. Hulleman et al. • International Conference on Learning Analytics and Knowledge (LAK) (2016)

Student intention and motivation are among the strongest predictors of persistence and completion in Massive Open Online Courses (MOOCs), but these factors are typically measured through fixed-response items that constrain student expressio...

OPENALEX DOI: https://doi.org/10.1145/2883851.2883932

Natural Language Processing

Santanu Pattanayak • Apress eBooks (2022)

In the modern age of information and analytics, natural language processing (NLP) is one of the most important technologies out there. Making sense of complex structures in language and deriving insights and actions from it are crucial from...

OPENALEX DOI: https://doi.org/10.1007/978-1-4842-8931-0_4

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

Jian Feng Guo, He He, Tong He, Leonard Lausen et al. • arXiv (Cornell University) (2019)

We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training l...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1907.04433