{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T05:59:58Z","timestamp":1770962398804,"version":"3.50.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Institute for Energy Technology"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Cybercriminals have increasingly adopted advanced and cutting-edge methods that expand the scale and speed of their attacks in recent years. This trend coincides with the rising demand for and scarcity of highly skilled cybersecurity specialists, making them both expensive and difficult to find. Recently, researchers have demonstrated the effectiveness of Artificial Intelligence (AI) approaches in combating sophisticated cyberattacks. However, comprehensive bibliometric data illustrating the study of AI approaches in cyberattack detection remain sparse. This study addresses this gap by investigating the current state of AI-based cyberattack detection research. The study analyzed the Scopus database using bibliometric analysis on a pool of over 2,338 articles published between 2014 and 2024, including 1217 journal articles, 828 conference papers, 121 conference reviews, 85 book chapters, 70 reviews, 5 editorials, and 2 books and short surveys. The study explores various AI-based cyberattack detection approaches globally, focusing on machine learning and deep learning algorithms. The bibliometric analysis was conducted using R, an open-source statistical tool, and Biblioshiny. The findings establish that AI, particularly machine learning and deep learning, enhances intrusion detection accuracy and is a growing research trend. Researchers have effectively employed these techniques for malware detection. The USA leads in AI cyberattack research, followed by India, China, Saudi Arabia, and Australia. Despite publishing fewer articles, Canada and Italy received significant citations. Additionally, strong research collaboration exists among the USA, China, Australia, Saudi Arabia, and India. Keyword analysis highlights AI\u2019s effectiveness in identifying patterns and malicious behaviours, enhancing intrusion detection even in complex cyberattacks. Machine learning can detect intrusions based on anomalies caused by malicious or compromised devices, as well as unknown threats, with speed, accuracy, and a low false-positive rate.<\/jats:p>","DOI":"10.1007\/s10462-025-11167-0","type":"journal-article","created":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T03:39:04Z","timestamp":1742614744000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Bibliometric analysis of artificial intelligence cyberattack detection models"],"prefix":"10.1007","volume":"58","author":[{"given":"Blessing","family":"Guembe","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjay","family":"Misra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ambrose","family":"Azeta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ines","family":"Lopez-Baldominos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,21]]},"reference":[{"issue":"2","key":"11167_CR1","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1109\/mcom.2018.1700332","volume":"56","author":"A Abeshu","year":"2018","unstructured":"Abeshu A, Chilamkurti N (2018) Deep learning: the frontier for distributed attack detection in Fog-to-Things computing. IEEE Commun Mag 56(2):169\u2013175. https:\/\/doi.org\/10.1109\/mcom.2018.1700332","journal-title":"IEEE Commun Mag"},{"key":"11167_CR2","doi-asserted-by":"publisher","first-page":"158","DOI":"10.58496\/MJCS\/2023\/018","volume":"2023","author":"OS Albahri","year":"2023","unstructured":"Albahri OS, AlAmoodi AH (2023) Cybersecurity and artificial intelligence applications: a bibliometric analysis based on scopus database. Mesopotamian J Cybersecur 2023:158\u2013169. https:\/\/doi.org\/10.58496\/MJCS\/2023\/018","journal-title":"Mesopotamian J Cybersecur"},{"key":"11167_CR3","doi-asserted-by":"publisher","first-page":"105124","DOI":"10.1016\/j.knosys.2019.105124","volume":"189","author":"A Aldweesh","year":"2020","unstructured":"Aldweesh A, Derhab A, Emam A (2020) Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. Knowl Based Syst 189:105124. https:\/\/doi.org\/10.1016\/j.knosys.2019.105124","journal-title":"Knowl Based Syst"},{"key":"11167_CR4","doi-asserted-by":"publisher","unstructured":"Alkhudaydi OA, Krichen M, Alghamdi AD (2023) A Deep Learning Methodology for Predicting Cybersecurity Attacks on the Internet of Things, Information, vol. 14, no. 10, p. 550. https:\/\/doi.org\/10.3390\/info14100550","DOI":"10.3390\/info14100550"},{"key":"11167_CR5","doi-asserted-by":"publisher","unstructured":"Alom M, Taha T, Yakopcic C, Westberg S, Sidike P, Nasrin M, Hasan M, Van BC, Awwal ASS, Asari VK (2019) A State-of-the-art survey on deep learning theory and architectures, Electronics, vol. 8, no. 3, p. 292, Mar. https:\/\/doi.org\/10.3390\/electronics8030292","DOI":"10.3390\/electronics8030292"},{"key":"11167_CR6","doi-asserted-by":"publisher","unstructured":"Alshehri A, Khan N, Alowayr A, Alghamdi MY (2022) Comput Syst Sci Eng 44(2):1679\u20131689. https:\/\/doi.org\/10.32604\/csse.2023.026526. cyberattack detection framework using machine learning and user behavior analytics,","DOI":"10.32604\/csse.2023.026526"},{"key":"11167_CR7","doi-asserted-by":"crossref","unstructured":"Antwarg L, Miller R, Shapira B, Rokach L (2021) Explaining anomalies detected by autoencoders using Shapley additive explanations, expert systems with applications. 186:115736.","DOI":"10.1016\/j.eswa.2021.115736"},{"key":"11167_CR8","doi-asserted-by":"publisher","unstructured":"Alrashdi I, Alqazzaz A, Aloufi E, Alharthi R, Zohdy M, Ming H (2019) AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning, in IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Jan. 2019, pp. 305\u2013310. https:\/\/doi.org\/10.1109\/CCWC.2019.8666450","DOI":"10.1109\/CCWC.2019.8666450"},{"issue":"4","key":"11167_CR9","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1016\/j.joi.2017.08.007","volume":"11","author":"M Aria","year":"2017","unstructured":"Aria M, Cuccurullo C (2017) Bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetrics 11(4):959\u2013975. https:\/\/doi.org\/10.1016\/j.joi.2017.08.007","journal-title":"J Informetrics"},{"key":"11167_CR10","doi-asserted-by":"publisher","unstructured":"Arpita M, Panchal S (2022) Smart health and cybersecurity in the era of artificial intelligence, in Information and Communication Technology for Competitive Strategies (ICTCS 2021), vol. 401, pp. 41\u201348. https:\/\/doi.org\/10.1007\/978-981-19-0098-3_5","DOI":"10.1007\/978-981-19-0098-3_5"},{"key":"11167_CR11","doi-asserted-by":"publisher","unstructured":"Arora P, Jain A (2021) Cybersecurity threats and their solutions through deep learning: A bibliometric analysis. In Proceedings of the 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 944\u2013 1949. IEEE. https:\/\/doi.org\/10.1109\/ICAC3N53548.2021.9725480","DOI":"10.1109\/ICAC3N53548.2021.9725480"},{"key":"11167_CR12","doi-asserted-by":"publisher","unstructured":"Atat R, Liu L, Wu J, Li G, Ye C, Yang Y (2018) IEEE Access 6:73603\u201373636. https:\/\/doi.org\/10.1109\/access.2018.2878681. Big Data Meet Cyber-Physical Systems: A Panoramic Survey,","DOI":"10.1109\/access.2018.2878681"},{"key":"11167_CR13","doi-asserted-by":"publisher","unstructured":"Awadallah AN (2021) enhancing network intrusion detection model using machine learning algorithms, Computers, Materials & Continua, vol. 67, no. 1, pp. 979\u2013990. https:\/\/doi.org\/10.32604\/cmc.2021.014307","DOI":"10.32604\/cmc.2021.014307"},{"key":"11167_CR14","doi-asserted-by":"publisher","unstructured":"Bhandari G, Lyth A, Shalaginov A, Gr\u00f8nli T-M (2023) Distributed deep neural- network-based middleware for cyber-attacks detection in smart iot ecosystem: a novel framework and performance evaluation approach, Electronics, vol. 12, no. 2, p. 298. https:\/\/doi.org\/10.3390\/electronics12020298","DOI":"10.3390\/electronics12020298"},{"key":"11167_CR15","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.patcog.2018.07.023","volume":"84","author":"B Biggio","year":"2018","unstructured":"Biggio B, Roli F (2018) Wild patterns: ten years after the rise of adversarial machine learning. Pattern Recogn 84:317\u2013331. https:\/\/doi.org\/10.1016\/j.patcog.2018.07.023","journal-title":"Pattern Recogn"},{"key":"11167_CR16","doi-asserted-by":"publisher","DOI":"10.24989\/ocg.v335.12","author":"I Cojocaru","year":"2022","unstructured":"Cojocaru I, Cojocaru I (2022) A bibliometric analysis of cybersecurity research papers in Eastern Europe: case study from the Republic of Moldova. Cent East Eur e|Dem e|Gov Days 151\u2013162. https:\/\/doi.org\/10.24989\/ocg.v335.12","journal-title":"Cent East Eur e|Dem e|Gov Days"},{"key":"11167_CR17","unstructured":"Dayyabu UY, Uppin C, Greenwood J (2020) A bibliometric analysis of cloud security research, Int. J. Comput. Eng. Technol., vol. 11, no. 4, pp. 1\u201312, Available: https:\/\/www.iaeme.com\/ijcet\/issues.asp?JType=IJCET&VType=11&IType=4"},{"key":"11167_CR18","doi-asserted-by":"publisher","unstructured":"Dehdarirad T, Villarroya A, Barrios M (2015) Research on women in science and higher education: a bibliometric analysis, Scientometrics, vol. 103, no. 3, pp. 795\u2013812. https:\/\/doi.org\/10.1007\/s11192-015-1574-x","DOI":"10.1007\/s11192-015-1574-x"},{"key":"11167_CR19","doi-asserted-by":"publisher","unstructured":"Demchak CC (2019) China: Determined to dominate cyberspace and AI, Bulletin of the Atomic Scientists, vol. 75, no. 3, pp. 99\u2013104. https:\/\/doi.org\/10.1080\/00963402.2019.1604857","DOI":"10.1080\/00963402.2019.1604857"},{"key":"11167_CR20","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1016\/j.future.2017.08.043","volume":"82","author":"A Diro","year":"2018","unstructured":"Diro A, Chilamkurti N (2018) Distributed attack detection scheme using deep learning approach for internet of things. Future Generation Comput Syst 82:761\u2013768. https:\/\/doi.org\/10.1016\/j.future.2017.08.043","journal-title":"Future Generation Comput Syst"},{"key":"11167_CR21","doi-asserted-by":"crossref","unstructured":"Erukala SB, Barthwal A, Kaluri R (2022) Sec-edge: trusted blockchain system for enabling the identification and authentication of edge based 5G networks, computer communications. 10\u201329. Advance online publication10.1016\/j.comcom.2022.12.001","DOI":"10.1016\/j.comcom.2022.12.001"},{"key":"11167_CR22","doi-asserted-by":"publisher","unstructured":"Fern\u00e1ndez-Caram\u00e9s TM, Fraga-Lamas P (2018) Towards the Internet of smart clothing: a review on iot wearables and garments for creating intelligent connected e-textiles, Electronics, vol. 7, no. 12, p. 405. https:\/\/doi.org\/10.3390\/electronics7120405","DOI":"10.3390\/electronics7120405"},{"key":"11167_CR23","doi-asserted-by":"publisher","first-page":"7700","DOI":"10.1109\/access.2018.2803446","volume":"6","author":"L Fernandez Maimo","year":"2018","unstructured":"Fernandez Maimo L, Perales Gomez A, Garcia Clemente F, Gil Perez M, Martinez Perez G (2018) A Self-Adaptive deep Learning-Based system for anomaly detection in 5G networks. IEEE Access 6:7700\u20137712. https:\/\/doi.org\/10.1109\/access.2018.2803446","journal-title":"IEEE Access"},{"key":"11167_CR24","doi-asserted-by":"publisher","unstructured":"Firdaus A, Razak M, Feizollah A, Hashem I, Hazim M, Anuar N (2019) The rise of \u2018blockchain\u2019: bibliometric analysis of blockchain study, Scientometrics, vol. 120, no. 3, pp. 1289\u20131331. https:\/\/doi.org\/10.1007\/s11192-019-03170-4","DOI":"10.1007\/s11192-019-03170-4"},{"key":"11167_CR25","doi-asserted-by":"publisher","first-page":"102767","DOI":"10.1016\/j.jnca.2020.102767","volume":"169","author":"S Gamage","year":"2020","unstructured":"Gamage S, Samarabandu J (2020) Deep learning methods in network intrusion detection: A survey and an objective comparison. J Netw Comput Appl 169:102767. https:\/\/doi.org\/10.1016\/j.jnca.2020.102767","journal-title":"J Netw Comput Appl"},{"issue":"6","key":"11167_CR26","first-page":"2567","volume":"7","author":"S Gargi","year":"2022","unstructured":"Gargi S, Saikat G (2022) Cyber security trend analysis using web of science: A bibliometric analysis. Eur J Mol Clin Med 7(6):2567\u20132576","journal-title":"Eur J Mol Clin Med"},{"key":"11167_CR27","doi-asserted-by":"publisher","unstructured":"Guembe B, Azeta A, Misra S, Osamor V, Fernandez-Sanz L, Pospelova V (2022) The emerging threat of AI-driven cyber attacks: a review, Applied Artificial Intelligence, pp. 1\u201334. https:\/\/doi.org\/10.1080\/08839514.2022.2037254","DOI":"10.1080\/08839514.2022.2037254"},{"key":"11167_CR28","doi-asserted-by":"publisher","first-page":"34564","DOI":"10.1109\/ACCESS.2020.2975142","volume":"8","author":"M Gupta","year":"2020","unstructured":"Gupta M, Abdelsalam M, Khorsandroo S, Mittal S (2020) Security and privacy in smart farming: challenges and opportunities. IEEE Access 8:34564\u201334584. https:\/\/doi.org\/10.1109\/ACCESS.2020.2975142","journal-title":"IEEE Access"},{"key":"11167_CR29","doi-asserted-by":"publisher","first-page":"100059","DOI":"10.1016\/j.iot.2019.100059","volume":"7","author":"M Hasan","year":"2019","unstructured":"Hasan M, Islam M, Zarif M, Hashem M (2019) Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet Things 7:100059. https:\/\/doi.org\/10.1016\/j.iot.2019.100059","journal-title":"Internet Things"},{"key":"11167_CR30","unstructured":"Hoadley DS, Lucas NJ (2024) Artificial Intelligence and National Security, Congressional Research Service Report, 7-5700 R45178, Version 3, May 8, Available: https:\/\/crsreports.congress.gov\/product\/details?prodcode=R45178"},{"issue":"6","key":"11167_CR31","doi-asserted-by":"publisher","first-page":"3179","DOI":"10.32604\/iasc.2021.014369","volume":"27","author":"R Kaluri","year":"2021","unstructured":"Kaluri R, Rajput DS, Xin Q, Lakshmanna K, Bhattacharya S, Gadekallu TR, Maddikunta PKR (2021) Roughsets-based approach for predicting battery life in IoT. Intell Autom Soft Comput 27(6):3179\u20133189. https:\/\/doi.org\/10.32604\/iasc.2021.014369","journal-title":"Intell Autom Soft Comput"},{"issue":"1","key":"11167_CR32","doi-asserted-by":"publisher","first-page":"20240153","DOI":"10.1515\/jisys-2024-0153","volume":"33","author":"YL Khaleel","year":"2024","unstructured":"Khaleel YL, Habeeb MA, Albahri AS, Al-Quraishi T, Albahri OS, Alamoodi AH (2024) Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods. J Intell Syst 33(1):20240153. https:\/\/doi.org\/10.1515\/jisys-2024-0153","journal-title":"J Intell Syst"},{"key":"11167_CR33","unstructured":"Kumar S, Kumar S (2008) Collaboration in research productivity in oil seed research institutes of India, in Proc. Fourth Int. Conf. Webometrics, Informetrics and Scientometrics, vol. 28, Jul"},{"key":"11167_CR34","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ad2492","author":"J Li","year":"2024","unstructured":"Li J, Li J, Wang C, Verbeek FJ, Schultz T, Liu H (2024) MS2OD: outlier detection using minimum spanning tree and medoid selection. Mach Learning: Sci Technol 5(1). https:\/\/doi.org\/10.1088\/2632-2153\/ad2492","journal-title":"Mach Learning: Sci Technol"},{"key":"11167_CR35","unstructured":"Li X, Xiong H, Li X, Wu X, Chen Z, Dou D (2022) InterpretDL: Explaining deep models in paddlepaddle, J. Mach. Learn. Res., vol. 23, no. 197, pp. 1\u20136, Available: http:\/\/jmlr.org\/papers\/v23\/21-0738.html"},{"key":"11167_CR36","doi-asserted-by":"publisher","first-page":"12103","DOI":"10.1109\/access.2018.2805680","volume":"6","author":"Q Liu","year":"2018","unstructured":"Liu Q, Li P, Zhao W, Cai W, Yu S, Leung V (2018) A survey on security threats and defensive techniques of machine learning: A data driven view. IEEE Access 6:12103\u201312117. https:\/\/doi.org\/10.1109\/access.2018.2805680","journal-title":"IEEE Access"},{"key":"11167_CR37","doi-asserted-by":"publisher","unstructured":"Makawana P, Jhaveri R (2017) A Bibliometric Analysis of Recent Research on Machine Learning for Cyber Security, in Intelligent Communication and Computational Technologies, pp. 213\u2013226. https:\/\/doi.org\/10.1007\/978-981-10-5523-2_20","DOI":"10.1007\/978-981-10-5523-2_20"},{"key":"11167_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/6634811","volume":"2021","author":"B Mahbooba","year":"2021","unstructured":"Mahbooba B, Timilsina M, Sahal R, Serrano M (2021) Explainable artificial intelligence (XAI) to enhance trust management in intrusion detection systems using decision tree model. Complexity 2021:1\u201311. https:\/\/doi.org\/10.1155\/2021\/6634811","journal-title":"Complexity"},{"key":"11167_CR39","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.neucom.2019.02.056","volume":"347","author":"S Mahdavifar","year":"2019","unstructured":"Mahdavifar S, Ghorbani A (2019) Application of deep learning to cybersecurity: A survey. Neurocomputing 347:149\u2013176. https:\/\/doi.org\/10.1016\/j.neucom.2019.02.056","journal-title":"Neurocomputing"},{"key":"11167_CR40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-45541-5_11","author":"S Nakhodchi","year":"2020","unstructured":"Nakhodchi S, Dehghantanha A (2020) A bibliometric analysis on the application of deep learning in cybersecurity. Secur Cyber-Physical Systems: Vulnerability Impact 203\u2013221. https:\/\/doi.org\/10.1007\/978-3-030-45541-5_11","journal-title":"Secur Cyber-Physical Systems: Vulnerability Impact"},{"key":"11167_CR41","doi-asserted-by":"publisher","first-page":"8852","DOI":"10.1007\/s11227-021-04250-0","volume":"78","author":"M Nasir","year":"2022","unstructured":"Nasir M, Javed AR, Tariq MA, Asim M, Baker T (2022) Feature engineering and deep learning-based intrusion detection framework for Securing edge IoT. J Supercomput 78:8852\u20138866. https:\/\/doi.org\/10.1007\/s11227-021-04250-0","journal-title":"J Supercomput"},{"key":"11167_CR42","doi-asserted-by":"publisher","unstructured":"Navya V, Adithi J, Rudrawal D, Tailor H, James N (2021) Intrusion Detection System using Deep Neural Networks (DNN), in International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India. https:\/\/doi.org\/10.1109\/icaeca52838.2021.9675513","DOI":"10.1109\/icaeca52838.2021.9675513"},{"key":"11167_CR43","doi-asserted-by":"publisher","first-page":"102104","DOI":"10.1016\/j.ijinfomgt.2020.102104","volume":"53","author":"R Nishant","year":"2020","unstructured":"Nishant R, Kennedy M, Corbett J (2020) Artificial intelligence for sustainability: challenges, opportunities, and a research agenda. Int J Inf Manag 53:102104. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2020.102104","journal-title":"Int J Inf Manag"},{"key":"11167_CR44","doi-asserted-by":"publisher","unstructured":"Nunes E, Diab A, Gunn A, Marin E, Mishra V, Paliath V, Robertson J, Shakarian J, Thart A, Shakarian P (2016) Darknet and deepnet mining for proactive cybersecurity threat intelligence, in IEEE Conference on Intelligence and Security Informatics (ISI), pp. 7\u2013\u20096. https:\/\/doi.org\/10.1109\/isi.2016.7745435","DOI":"10.1109\/isi.2016.7745435"},{"key":"11167_CR45","doi-asserted-by":"publisher","unstructured":"Oakleaf M (2010) Writing Information Literacy Assessment Plans: A Guide to Best Practice, Comminfolit, vol. 3, no. 2, p. 80. https:\/\/doi.org\/10.15760\/comminfolit.2010.3.2.73","DOI":"10.15760\/comminfolit.2010.3.2.73"},{"key":"11167_CR46","doi-asserted-by":"publisher","DOI":"10.1080\/08839514.2024.2439609","author":"L Ofusori","year":"2024","unstructured":"Ofusori L, Bokaba T, Mhlongo S (2024) Artificial intelligence in cybersecurity: A comprehensive review and future direction. Appl Artif Intell 38(1). https:\/\/doi.org\/10.1080\/08839514.2024.2439609","journal-title":"Appl Artif Intell"},{"issue":"1","key":"11167_CR47","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1109\/TITS.2022.3188671","volume":"24","author":"A Oseni","year":"2023","unstructured":"Oseni A, Moustafa N, Creech G, Sohrabi N, Strelzoff A, Tari Z, Linkov I (2023) An explainable deep learning framework for resilient intrusion detection in IoT-Enabled transportation networks. IEEE Trans Intell Transp Syst 24(1):1000\u20131014. https:\/\/doi.org\/10.1109\/TITS.2022.3188671","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"11167_CR48","unstructured":"Pupillo L, Fantin S, Ferreira A, Polito C (2022) Artificial intelligence and cybersecurity, CEPS, Available: https:\/\/www.ceps.eu\/ceps-publications\/artificial-intelligence-and-cybersecurity-2\/. [Accessed: Feb. 8, 2022]"},{"key":"11167_CR49","unstructured":"Rakhsha A, Radanovic G, Devidze R, Zhu X, Singla A (2021) Policy teaching in reinforcement learning via environment poisoning attacks, J. Mach. Learn. Res., vol. 22, no. 210, pp. 1\u201345, Available: https:\/\/www.jmlr.org\/papers\/v22\/20-1329.html"},{"key":"11167_CR50","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.jnca.2016.08.022","volume":"75","author":"M Razak","year":"2016","unstructured":"Razak M, Anuar N, Salleh R, Firdaus A (2016) The rise of \u2018malware\u2019: bibliometric analysis of malware study. J Netw Comput Appl 75:58\u201376. https:\/\/doi.org\/10.1016\/j.jnca.2016.08.022","journal-title":"J Netw Comput Appl"},{"key":"11167_CR51","doi-asserted-by":"publisher","unstructured":"Rahim N Bibliometric Analysis of Cyber Threat and Cyber Attack Literature: Exploring the Higher Education Context, in Cybersecurity Threats with New Perspectives, IntechOpen, 2021. Available: https:\/\/doi.org\/10.5772\/intechopen.98038","DOI":"10.5772\/intechopen.98038"},{"key":"11167_CR52","doi-asserted-by":"publisher","first-page":"10127","DOI":"10.1109\/access.2018.2890507","volume":"7","author":"K Salah","year":"2019","unstructured":"Salah K, Rehman M, Nizamuddin N, Al-Fuqaha A (2019) Blockchain for AI: review and open research challenges. IEEE Access 7:10127\u201310149. https:\/\/doi.org\/10.1109\/access.2018.2890507","journal-title":"IEEE Access"},{"key":"11167_CR53","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-021-00592-x","author":"IH Sarker","year":"2021","unstructured":"Sarker IH (2021) Machine learning: algorithms, Real-World applications and research directions. SN Comput Sci 2(3). https:\/\/doi.org\/10.1007\/s42979-021-00592-x","journal-title":"SN Comput Sci"},{"key":"11167_CR54","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00318-5","author":"IH Sarker","year":"2020","unstructured":"Sarker IH, Kayes A, Badsha S, Alqahtani H, Watters P, Ng A (2020) Cybersecurity data science: an overview from machine learning perspective. J Big Data 7(1). https:\/\/doi.org\/10.1186\/s40537-020-00318-5","journal-title":"J Big Data"},{"key":"11167_CR55","doi-asserted-by":"publisher","unstructured":"Schmidt E (2022) AI, great power competition & national security, Daedalus, vol. 151, no. 2, pp. 288\u2013298. https:\/\/doi.org\/10.1162\/daed_a_01916","DOI":"10.1162\/daed_a_01916"},{"key":"11167_CR56","unstructured":"SETS, CYBSEC4AI Task force report, society for electronic transactions and security (SETS) under Office of the Principal Scientific Adviser to the Government of India (2020). Accessed: May 9, 2024"},{"key":"11167_CR57","doi-asserted-by":"publisher","first-page":"81265","DOI":"10.1109\/ACCESS.2024.3411632","volume":"12","author":"R Shevchuk","year":"2024","unstructured":"Shevchuk R, Martsenyuk V (2024) Neural networks toward cybersecurity: domain map analysis of state-of-the-art challenges. IEEE Access 12:81265\u201381280. https:\/\/doi.org\/10.1109\/ACCESS.2024.3411632","journal-title":"IEEE Access"},{"key":"11167_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4301\/s1807-1775201815004","volume":"15","author":"SK Srivastava","year":"2018","unstructured":"Srivastava SK (2018) artificial intelligence: way forward for India. J Inform Syst Technol Manage 15:1\u201323. https:\/\/doi.org\/10.4301\/s1807-1775201815004","journal-title":"J Inform Syst Technol Manage"},{"key":"11167_CR59","doi-asserted-by":"publisher","first-page":"107138","DOI":"10.1016\/j.comnet.2020.107138","volume":"171","author":"D Vasan","year":"2020","unstructured":"Vasan D, Alazab M, Wassan S, Naeem H, Safaei B, Zheng Q (2020) IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture. Comput Netw 171:107138. https:\/\/doi.org\/10.1016\/j.comnet.2020.107138","journal-title":"Comput Netw"},{"key":"11167_CR60","doi-asserted-by":"publisher","unstructured":"Wang Q, Guo W, Zhang K, Ororbia A, Xing X, Liu X, Giles C adversary resistant deep neural networks with an application to malware detection, in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, pp. 1145\u20131153. https:\/\/doi.org\/10.1145\/3097983.3098158","DOI":"10.1145\/3097983.3098158"},{"key":"11167_CR61","doi-asserted-by":"publisher","unstructured":"Wu X, Chen X, Zhan F, Hong S (2015) Global research trends in landslides during 1991\u2013 2014: a bibliometric analysis, Landslides, vol. 12, no. 6, pp. 1215\u20131226. https:\/\/doi.org\/10.1007\/s10346-015-0624-z","DOI":"10.1007\/s10346-015-0624-z"},{"key":"11167_CR62","doi-asserted-by":"publisher","first-page":"35365","DOI":"10.1109\/access.2018.2836950","volume":"6","author":"Y Xin","year":"2018","unstructured":"Xin Y, Kong L, Liu Z, Chen Y, Li Y, Zhu H, Goa M, Hou H, Wang C (2018) Machine learning and deep learning methods for cybersecurity. IEEE Access 6:35365\u201335381. https:\/\/doi.org\/10.1109\/access.2018.2836950","journal-title":"IEEE Access"},{"key":"11167_CR63","doi-asserted-by":"publisher","unstructured":"Yang G (2014) The return of ideology and the future of chinese internet policy, Critical Studies in Media Communication, vol. 31, no. 2, pp. 109\u2013113. https:\/\/doi.org\/10.1080\/15295036.2014.913803","DOI":"10.1080\/15295036.2014.913803"},{"key":"11167_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.26466\/opusjsr.1063227","volume":"19","author":"B Yildiz","year":"2022","unstructured":"Yildiz B, Younes GE (2022) Cyber-Physical systems and cyber security: A bibliometric analysis. OPUS J Soc Res 19:1\u20131. https:\/\/doi.org\/10.26466\/opusjsr.1063227","journal-title":"OPUS J Soc Res"},{"key":"11167_CR65","doi-asserted-by":"publisher","unstructured":"Yu J, Lu L, Chen Y, Zhu Y, Kong L (2021) An Indirect Eavesdropping Attack of Keystrokes on Touch Screen through Acoustic Sensing, in IEEE Transactions on Mobile Computing, vol. 20, no. 2, pp. 337\u2013351, 1 Feb. https:\/\/doi.org\/10.1109\/TMC.2019.2947468","DOI":"10.1109\/TMC.2019.2947468"},{"key":"11167_CR66","unstructured":"Zahavy T, Ben-Zrihem N, Mannor S (2016) Graying the black box: Understanding DQNs, in Proceedings of the International Conference on Machine Learning, New York, NY, USA, pp. 1899\u20131908"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11167-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-025-11167-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11167-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T19:34:43Z","timestamp":1744918483000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-025-11167-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,21]]},"references-count":66,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["11167"],"URL":"https:\/\/doi.org\/10.1007\/s10462-025-11167-0","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,21]]},"assertion":[{"value":"28 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"No table or Figure is taken from any sources. Not required.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"177"}}