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This comprehensive review examines the role of artificial intelligence (AI) in enhancing threat detection and cybersecurity, focusing on recent advancements and ongoing challenges in this dynamic field. The ability to identify and counteract cybersecurity threats including network breaches, adversarial assaults, and zero-day vulnerabilities has significantly increased with the inclusion of AI, especially machine learning and deep learning techniques. The review underscores the critical role of explainability and resilience in AI models to ensure trustworthiness and reliability in AI-driven security solutions. The studies analyzed span a wide range of sectors, including Industry 5.0, the Internet of Things (IoT), 5G networks, and autonomous vehicles, illustrating AI’s adaptability in tackling unique security issues across these domains. Cutting-edge approaches, such as transformer-based models, federated learning, and blockchain integration, are advancing the development of more robust and real-time threat detection systems. However, challenges persist, particularly in managing large-scale data, enabling real-time processing, and ensuring privacy and security. The review concludes that although substantial progress has been achieved, ongoing research and collaboration are vital to fully harness AI’s potential in securing digital landscapes.
Kavitha et al. (Mon,) studied this question.