Leveraging AI for enhanced cybersecurity: a comprehensive review
Abstract Cybersecurity has become a serious concern in this digital age, with ever-increasing threats aimed at individuals, enterprises, and countries across the globe. AI has been rapidly emerging as an influential technology that possess enormous abilities to enhance data protection measures by au...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06773-0 |
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| author | Thammisetty Swetha U. Kumaran V. P. Meena Ibrahim A. Hameed |
| author_facet | Thammisetty Swetha U. Kumaran V. P. Meena Ibrahim A. Hameed |
| author_sort | Thammisetty Swetha |
| collection | DOAJ |
| description | Abstract Cybersecurity has become a serious concern in this digital age, with ever-increasing threats aimed at individuals, enterprises, and countries across the globe. AI has been rapidly emerging as an influential technology that possess enormous abilities to enhance data protection measures by automating threat recognition, vulnerability assessment, and incident response. This study focuses on comprehensively examining current literature works dedicated to AI along with its applications in cybersecurity and presents an innovative architecture to deal with challenges that pose enormous risks to worlds community. Explore advanced techniques, including Machine learning (ML) and deep learning (DL), and their implementation in threat intelligence, intrusion recognition, malware analysis, phishing detection, and user authentication. Furthermore, the propounded review deliberates on various limitations such as data scarcity, model interpretability, and vulnerability to adversarial attacks can hinder the effectiveness of existing models in real-world cybersecurity scenarios, leading to reduced accuracy, slower response times, and increased susceptibility to evasion techniques. The future advances, including the evolution of explainable AI, predictive analytics, quantum computing, and real-time management capabilities, are also emphasized. This review paper is intended to offer a systematic understanding of modern AI techniques and prospective solutions in enriching the cybersecurity domain, offering valuable insights for researcher scholars and professionals in diverse fields. |
| format | Article |
| id | doaj-art-b7af1e8b0d7643a999620ec5f64b39ae |
| institution | OA Journals |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-b7af1e8b0d7643a999620ec5f64b39ae2025-08-20T02:05:14ZengSpringerDiscover Applied Sciences3004-92612025-06-017612710.1007/s42452-025-06773-0Leveraging AI for enhanced cybersecurity: a comprehensive reviewThammisetty Swetha0U. Kumaran1V. P. Meena2Ibrahim A. Hameed3Department of Computer Science and Engineering, Amrita School of Computing, Bengaluru, Amrita Vishwa VidyapeethamDepartment of Computer Science and Engineering, Amrita School of Computing, Bengaluru, Amrita Vishwa VidyapeethamDepartment of Electrical Engineering, National Institute of Technology JamshedpurDepartment of ICT and Natural Sciences, Norwegian University of Science and TechnologyAbstract Cybersecurity has become a serious concern in this digital age, with ever-increasing threats aimed at individuals, enterprises, and countries across the globe. AI has been rapidly emerging as an influential technology that possess enormous abilities to enhance data protection measures by automating threat recognition, vulnerability assessment, and incident response. This study focuses on comprehensively examining current literature works dedicated to AI along with its applications in cybersecurity and presents an innovative architecture to deal with challenges that pose enormous risks to worlds community. Explore advanced techniques, including Machine learning (ML) and deep learning (DL), and their implementation in threat intelligence, intrusion recognition, malware analysis, phishing detection, and user authentication. Furthermore, the propounded review deliberates on various limitations such as data scarcity, model interpretability, and vulnerability to adversarial attacks can hinder the effectiveness of existing models in real-world cybersecurity scenarios, leading to reduced accuracy, slower response times, and increased susceptibility to evasion techniques. The future advances, including the evolution of explainable AI, predictive analytics, quantum computing, and real-time management capabilities, are also emphasized. This review paper is intended to offer a systematic understanding of modern AI techniques and prospective solutions in enriching the cybersecurity domain, offering valuable insights for researcher scholars and professionals in diverse fields.https://doi.org/10.1007/s42452-025-06773-0CybersecurityMLDLIntrusion detectionMalware analysisPhishing detection |
| spellingShingle | Thammisetty Swetha U. Kumaran V. P. Meena Ibrahim A. Hameed Leveraging AI for enhanced cybersecurity: a comprehensive review Discover Applied Sciences Cybersecurity ML DL Intrusion detection Malware analysis Phishing detection |
| title | Leveraging AI for enhanced cybersecurity: a comprehensive review |
| title_full | Leveraging AI for enhanced cybersecurity: a comprehensive review |
| title_fullStr | Leveraging AI for enhanced cybersecurity: a comprehensive review |
| title_full_unstemmed | Leveraging AI for enhanced cybersecurity: a comprehensive review |
| title_short | Leveraging AI for enhanced cybersecurity: a comprehensive review |
| title_sort | leveraging ai for enhanced cybersecurity a comprehensive review |
| topic | Cybersecurity ML DL Intrusion detection Malware analysis Phishing detection |
| url | https://doi.org/10.1007/s42452-025-06773-0 |
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