Cyber Security Threats Detection and Protection in IoT Devices Using S-Run-Based Deep Neural Network
Pragati Rana, B. P. Patil · 2024
Here, S-Run Based Deep Neural Network is developed to recognize Cyber Security Threats Detection and Protection in the Internet of Things (IoT). Initially, the information is pre-processed in which Data cleaning and normalization are carried out. This makes efficient classification. Afterward, the Sigmoid-based Krill heard algorithm-based feature selection approach is applied for the selection of the most relevant features for each kind of attack class. By doing this, the dimensionality of the features is decreased, which shortens the classifier's computation time. Lastly, a complex method of threat detection in an IoT context is employed by Cyber Security Threats Detection and Protection employing an S-Run Based Deep Neural Network (SR-DNN). This comprehensive strategy aims to fortify the security infrastructure against evolving cyber threats in the realm of IoT.