Screening Cyberattacks and Fraud via Heterogeneous Layering
Abdulrahman M. Alahmadi · International Journal of Advanced Computer Science and Applications · 2024
On the Internet of Things (IoT) age, intelligent equipment is employed to give effective and dependable utilization of applications. IoT devices may recognize and provide extensive information while also intelligently processing that data. Data systems, systems for control, plus sensing are growing increasingly vital in contemporary manufacturing processes. The amount of internet of things gadgets and methods used is growing, that has culminated in a rise in assaults. Such assaults have the potential to interrupt international activities and cause major financial losses. Multiple methods, including Machine learning (ML) in addition to Deep Learning (DL), are being utilized for identifying cyberattack. In this investigation, researchers offer an ensemble staking approach that is strong strategy in ML for detecting assaults via the Internet of Things having excellent accuracy. Tests were carried out using three distinct information: credit card data, NSL-KDD, and UNSW. Single fundamental classifications were beaten by the suggested layered ensembles classification. The results show that the cyberattack detection model in this research possessed a 95.15% accuracy percentage, while the credit card fraud detection model achieved a 93.50% accuracy percentage.