Cognitive Digital Twin Systems for Predictive Security in AI-Enhanced IoT Environments

The Internet has grown in importance and impact over the years, causing people to become more reliant on it. The Internet has evolved into a major vector for cybercrime because to its ever-increasing user base. Over the last decade, the number of these computing systems—including desktops, laptops, smartphones, and the Internet of Things (IoT)—has skyrocketed. Among them, cell phones are practically integral to modern life. The popularity of web-based assaults has skyrocketed with the exponential growth in the number of individuals using the Internet. These web-based assaults are increasingly being combatted by security corporations. Unfortunately, new forms of these assaults are appearing all the time, making it hard for older security measures to stay up. Artificial intelligence (AI) is a source of optimism in the current cybersecurity landscape, offering a potential solution to the ever-changing digital dangers. The fast development of AI over the last decade has given rise to this optimism, because it is now impacting the expansion of every industry. With AI bringing so many advantages in every field, online security is one sector that just cannot afford to ignore it. This thesis represents progress in that direction. This thesis covers research that aims to use AI to tackle significant online security challenges. Security in Internet of Things (IoT) settings powered by artificial intelligence may be improved with the help of cognitive digital twin systems (CDTS). In order to provide proactive security measures, real-time monitoring, and predictive analysis, these systems use cutting-edge AI methods to digitally represent physical IoT devices. In order to anticipate and lessen the impact of security risks, this research introduces a new CDTS architecture that combines cognitive learning skills, anomaly detection, and machine learning models. The suggested approach reduced reaction time to security events by 87.5% and identified zero-day threats with a detection accuracy of $\mathbf{9 2. 4 \%}$ by using cognitive computing and predictive security measures. The findings prove that the CDTS architecture is a strong answer to adaptive and intelligent threat management, and they show how well it secures complicated IoT networks.