Using Optimized Artificial Intelligence Techniques to Prevent Cyber Security with the Internet of Things

The Internet of Things (IoT) provides high levels of security for physical items like smart appliances and household appliances. The Internet Protocol (IP) gives each physical item a distinct online address that it may use to communicate with other devices on the network and the outside world via the internet. As the number of hacker attacks on data transmission over the internet continues to climb, there is a growing concern about cybersecurity vulnerabilities in IoT devices. To construct a reliable Cyber Security (CS) system in the face of such potent attacks, attack detection is essential. Common threats to IoT systems include data-type probing, Denial-of-Service (DoS), and User-to-Root (U2R) attacks. Unfortunately, current methods for detecting and investigating IoT malware are insufficient. DoS attacks occur in IoT settings due to inadequate security monitoring and preventive actions. In order to predict attacks as well as problems with IoT devices, this article examines a number of performance-based Artificial Intelligence (AI) algorithms. Several improved optimisation approaches, particularly particle swarm optimisation techniques, were used to determine the productivity of the proposed AI strategy in detail for four different parameters. Hence, this article combines a machine learning method with an optimization algorithm to perform efficient feature extraction. The proposed method's efficiency is shown by relating its outcomes to those of the existing systems.