부산대학교 도서관

The implementation of quantum learning strategies is turning out to be increasingly crucial. When it comes to cybersecurity, one of the key benefits of using machine learning is that it makes the detection of malware more effective, scalable, and actionable than traditional human approaches. This is one of the primary advantages of employing machine learning. The cybersecurity risks posed by quantum learning must be effectively managed on a logical and theoretical level in order to be mitigated. It is necessary to prevail over these obstacles. Deep learning, support vector quantums, and Bayesian classification are just a few examples of the quantum learning and statistical technologies that have showed promise in the area of reducing the consequences of cyberattacks. When designing intelligent security systems, it is essential to unearth previously unknown patterns and insights hidden within network data, as well as to develop a data-driven quantum learning model to counteract the threats posed by these attacks. Additionally, it is essential to uncover previously unknown patterns and insights hidden within network data. The chapter develops AIbased modeling to improve the detection of cybersecurity attacks in Internet of Things--Software-Defined Network (IoT-SDN). The collection of network logs, preprocessing, and classification of instances enables the model to classify the attacks from the network. The simulation is conducted in Python to test the effectiveness of the AI-driven model. The results show that the proposed method achieves higher rate of accuracy in detecting the instances than other methods.