Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions
IntroductionThe rapid escalation of cyber threats necessitates innovative strategies to enhance cybersecurity and privacy measures. Artificial Intelligence (AI) has emerged as a promising tool poised to enhance the effectiveness of cybersecurity strategies by offering advanced capabilities for intru...
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| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Big Data |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2024.1497535/full |
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| author | Krishnashree Achuthan Sasangan Ramanathan Sethuraman Srinivas Raghu Raman |
| author_facet | Krishnashree Achuthan Sasangan Ramanathan Sethuraman Srinivas Raghu Raman |
| author_sort | Krishnashree Achuthan |
| collection | DOAJ |
| description | IntroductionThe rapid escalation of cyber threats necessitates innovative strategies to enhance cybersecurity and privacy measures. Artificial Intelligence (AI) has emerged as a promising tool poised to enhance the effectiveness of cybersecurity strategies by offering advanced capabilities for intrusion detection, malware classification, and privacy preservation. However, this work addresses the significant lack of a comprehensive synthesis of AI's use in cybersecurity and privacy across the vast literature, aiming to identify existing gaps and guide further progress.MethodsThis study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework for a comprehensive literature review, analyzing over 9,350 publications from 2004 to 2023. Utilizing BERTopic modeling, 14 key themes in AI-driven cybersecurity were identified. Topics were clustered and validated through a combination of algorithmic and expert-driven evaluations, focusing on semantic relationships and coherence scores.ResultsAI applications in cybersecurity are concentrated around intrusion detection, malware classification, federated learning in privacy, IoT security, UAV systems and DDoS mitigation. Emerging fields such as adversarial machine learning, blockchain and deep learning are gaining traction. Analysis reveals that AI's adaptability and scalability are critical for addressing evolving threats. Global trends indicate significant contributions from the US, India, UK, and China, highlighting geographical diversity in research priorities.DiscussionWhile AI enhances cybersecurity efficacy, challenges such as computational resource demands, adversarial vulnerabilities, and ethical concerns persist. More research in trustworthy AI, standardizing AI-driven methods, legislations for robust privacy protection amongst others is emphasized. The study also highlights key current and future areas of focus, including quantum machine learning, explainable AI, integrating humanized AI and deepfakes. |
| format | Article |
| id | doaj-art-5ffbfd99d4c3441b8d83c92179303f2d |
| institution | Kabale University |
| issn | 2624-909X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj-art-5ffbfd99d4c3441b8d83c92179303f2d2024-12-05T14:10:51ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2024-12-01710.3389/fdata.2024.14975351497535Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directionsKrishnashree Achuthan0Sasangan Ramanathan1Sethuraman Srinivas2Raghu Raman3Center for Cybersecurity Systems and Networks, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, Kerala, IndiaSchool of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, IndiaSchool of Engineering and Computer Science, University of Pacific, Stockton, CA, United StatesSchool of Business, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, Kerala, IndiaIntroductionThe rapid escalation of cyber threats necessitates innovative strategies to enhance cybersecurity and privacy measures. Artificial Intelligence (AI) has emerged as a promising tool poised to enhance the effectiveness of cybersecurity strategies by offering advanced capabilities for intrusion detection, malware classification, and privacy preservation. However, this work addresses the significant lack of a comprehensive synthesis of AI's use in cybersecurity and privacy across the vast literature, aiming to identify existing gaps and guide further progress.MethodsThis study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework for a comprehensive literature review, analyzing over 9,350 publications from 2004 to 2023. Utilizing BERTopic modeling, 14 key themes in AI-driven cybersecurity were identified. Topics were clustered and validated through a combination of algorithmic and expert-driven evaluations, focusing on semantic relationships and coherence scores.ResultsAI applications in cybersecurity are concentrated around intrusion detection, malware classification, federated learning in privacy, IoT security, UAV systems and DDoS mitigation. Emerging fields such as adversarial machine learning, blockchain and deep learning are gaining traction. Analysis reveals that AI's adaptability and scalability are critical for addressing evolving threats. Global trends indicate significant contributions from the US, India, UK, and China, highlighting geographical diversity in research priorities.DiscussionWhile AI enhances cybersecurity efficacy, challenges such as computational resource demands, adversarial vulnerabilities, and ethical concerns persist. More research in trustworthy AI, standardizing AI-driven methods, legislations for robust privacy protection amongst others is emphasized. The study also highlights key current and future areas of focus, including quantum machine learning, explainable AI, integrating humanized AI and deepfakes.https://www.frontiersin.org/articles/10.3389/fdata.2024.1497535/fullartificial intelligencecybersecurityprivacytopic modelingethicsquantum |
| spellingShingle | Krishnashree Achuthan Sasangan Ramanathan Sethuraman Srinivas Raghu Raman Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions Frontiers in Big Data artificial intelligence cybersecurity privacy topic modeling ethics quantum |
| title | Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions |
| title_full | Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions |
| title_fullStr | Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions |
| title_full_unstemmed | Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions |
| title_short | Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions |
| title_sort | advancing cybersecurity and privacy with artificial intelligence current trends and future research directions |
| topic | artificial intelligence cybersecurity privacy topic modeling ethics quantum |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2024.1497535/full |
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