Achuthan Krishnashree, Ramanathan Sasangan, Srinivas Sethuraman, Raman Raghu
Center for Cybersecurity Systems and Networks, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, Kerala, India.
School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India.
Front Big Data. 2024 Dec 5;7:1497535. doi: 10.3389/fdata.2024.1497535. eCollection 2024.
The 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.
This 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.
AI 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.
While 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.
网络威胁的迅速升级需要创新策略来加强网络安全和隐私保护措施。人工智能（AI）已成为一种有前途的工具，有望通过提供入侵检测、恶意软件分类和隐私保护等先进功能来提高网络安全策略的有效性。然而，这项工作旨在解决在大量文献中人工智能在网络安全和隐私方面的应用缺乏全面综合的问题，以识别现有差距并指导进一步的进展。
本研究采用系统评价和荟萃分析的首选报告项目（PRISMA）框架进行全面的文献综述，分析了2004年至2023年期间的9350多篇出版物。利用BERTopic建模，确定了人工智能驱动的网络安全中的14个关键主题。通过算法和专家驱动评估相结合的方式对主题进行聚类和验证，重点关注语义关系和连贯分数。
人工智能在网络安全中的应用集中在入侵检测、恶意软件分类、隐私方面的联邦学习、物联网安全、无人机系统和分布式拒绝服务缓解。对抗性机器学习、区块链和深度学习等新兴领域正越来越受到关注。分析表明，人工智能的适应性和可扩展性对于应对不断演变的威胁至关重要。全球趋势表明，美国、印度、英国和中国做出了重大贡献，突出了研究重点的地域多样性。
虽然人工智能提高了网络安全效能，但计算资源需求、对抗性漏洞和伦理问题等挑战依然存在。强调了在可信人工智能、标准化人工智能驱动方法、强有力的隐私保护立法等方面进行更多研究。该研究还突出了当前和未来的关键重点领域，包括量子机器学习、可解释人工智能、整合人性化人工智能和深度伪造。
