STUDY OF BANKING SECURITY USING CLOUD COMPUTING USING UNSUPERVISED LEARNING

Abstract– The Internet of Things (IoT) is a relatively new technology that was created with the goal of connecting and interconnecting all of the objects that are located worldwide. This technology makes it possible to achieve an astounding level of intelligence, which is beneficial to humankind in many different ways. Connecting the things that are all around us and giving them the ability to interact with one another is a step toward the goal of developing intelligent applications in the fields of healthcare, safety, and industrial operations. As a result of the Internet of things involving a large number of distinct organisations and a wide variety of applications, the likelihood of unwanted access occurring is significantly increased. As of right now, the cyberattacks that communication networks have to deal with are extremely dangerous and really scary. This research provides an intelligent approach or methodology to defend the security breach, produced with the enhancement of Deep Learning algorithms (Deep Belief Network), i.e., Deep Belief Network. The research was carried out by the Deep Belief Network. This intelligent intrusion detection methodology examines the harmful behaviour that is taking place within the network, and it makes an attempt to get access to where it is taking place. Within the scope of this study, the investigation into embedding the Deep Learning approach is the topic of discussion. Comparisons are made between the DBN upgrade to the security network and the DGAs and IDS algorithms that are considered industry standards, and the results are assessed.