[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117581-en":3,"doc-seo-117581-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117581,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Cybersecurity challenges and opportunities of machine learning-based artificial intelligence","Artificial intelligence, machine learning, and cybersecurity form the basis of this study, which examines how machine learning-based artificial intelligence can be integrated into cybersecurity use cases. The work surveys recent literature to summarize major challenges and opportunities created by such implementations and evaluates how selected issues influence the real effectiveness of resulting models. An experimental evaluation is conducted on a real-world cybersecurity system to demonstrate practical deployment and highlight implementation risks.","REVIEW  \nCybersecurity challenges and opportunities of machine learning-based artificial intelligence  \nPawel Czaja 1 • Bartlomiej Gdowski 1 • Marcin Niemiec 1 • Wim Mees2 •  \nNikolai Stoianov3 • Konstantinos Votis4 • Vyacheslav Kharchenko5 • Vasilis Katos6 • Matteo Merialdo7  \nReceived: 8 February 2025/Accepted: 22 August 2025/Published online: 8 October 2025  \n􀀂 The Author(s) 2025  \nAbstract  \nArtificial intelligence, machine learning, and cybersecurity are the topics of discussion of contemporary information technology sector and computing research. This study investigates the integration of machine learningbased artificial intelligence in the context of cybersecurity. This paper presents an overview of the recent literature, focusing on selected popular areas related to the challenges and opportunities that such implementations introduce. The authors also assess how selected problems related to the application of machine learning algorithms affect the real effectiveness represented by the resulting models. To support this analysis, an experimental study was conducted using a real-world cybersecurity system. This demonstration illustrates the practical implementation of a machine learning-based software solution in cybersecurity and highlights the potential challenges encountered during such implementations.  \nKeywords Cybersecurity 􀀂 Artificial intelligence 􀀂 Machine learning 􀀂 Intrusion detection Mathematics Subject Classification 68M25 􀀂 68T01 􀀂 68T05  \n1 Introduction  \nIn general, artificial intelligence (AI) is the ability of algorithms to take action—based on a previously conducted learning process—for cases that have not been precisely defined. Unlike commonly used algorithms in programming, AI allows searching for patterns in a set of processed data, which can then be used for new classification, regression, or clustering of data. The unique opportunities that such systems provide, the ease of implementation or integration of newer approaches, and the overall accessibility for—even a non-technical user—all add up to a huge gain of popularity of AI-based solutions. This is especially significant in the present and coming times, due to the increase in the volume of data exchanged between devices, the number of which is also growing rapidly. For this reason, one can observe increase of AI-oriented solutions usage in the context of cybersecurity.  \nWhile Artificial Super Intelligence—a type of intelligence far beyond the human brain’s ability to process data, cognition, and abstract thinking—is currently only a hypothetical concept, and Artificial General Intelligence—a system capable of solving a wide range of problems, matching the human brain in terms of abstract thinking—has no practical implementations, we have a broad range of usable AI solutions. For example, a group of algorithmbased solutions called Machine Learning (ML) is being widely implemented in Internet of Things (IoT) solutions,  \nNeural Computing and Applications (2025) 37:27931–27956  \n[https://doi.org/10.1007/s00521-025-1](https://doi.org/10.1007/s00521-025-1)1604-9  \ncloud environments, etc. This paper provides a review of both certain challenges as well as potential opportunities of leveraging ML-based AI solutions to solve the problems associated with cybersecurity.  \nThis study aims to conduct a set of tests and a review of scientific publications focused on selected areas of the topic of using ML-based AI in the field of cybersecurity. In particular, in the context of the challenges and opportunities that this topic introduces. The core of the study will be split into two main sections oriented on the following questions.  \n• What are the cybersecurity-related challenges of Machine Learning-based artificial intelligence implementations?  \n• What are the opportunities of Machine Learning-based artificial intelligence application in cybersecurity solutions?  \n2 Methods  \nTo answer the aforementioned questions, a comprehensive literature search was performed. 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