Advancements in Cybersecurity: A Comprehensive Survey of AI-Driven Solutions

Industrial Internet of Things (IIoT) is transforming industries through real-time data and automation, but it also introduces significant cybersecurity challenges. A cyberthreat is any malevolent attempt to jeopardise the security of linked industrial systems and devices. It can interfere with vital operations, cause equipment failure, data breaches, or control system manipulation, which can lead to serious financial losses, production halts, and safety hazards in sectors like manufacturing, energy, and transportation. In order to combat these changing threats, cybersecurity must be improved using AI-driven methods that allow for quicker threat detection, flexible responses, and proactive protection against advanced attacks. AI solutions enhance IIoT security with real-time anomaly detection, predictive analytics, automated responses, intelligent access control. Researchers have worked extensively in the domain of cybersecurity, developing various models for detecting cyberthreats and assessing their severity using advanced algorithms. This work provides a comprehensive and well-structured analysis of the developments in this field, highlighting the different methods that writers have created for identifying and averting cyberthreats. A performance comparison of these algorithms is also included. In addition, the study looks at the datasets that academics use for cybersecurity apps. Finally, the section on conclusion discusses the difficulties in the field of cybersecurity and suggests possible avenues for further study.