A Security Risk Model for Online Banking System

We are living in an era, where the security of our data has become a huge issue. The services of cyber are the most entertaining and time-saving things in one's life. Though, people save their data on the cloud, which has been managed by the cyber. Cyber-security plays an important role in this matter. This is the open challenge for the security because many intruders are there who can attack the data and hack the details of the user by the server. If we will see around, then we will find many cases which include cyber-crime. This has become a very genuine concern to secure our datasets which are available on the cloud. Our research includes the security of the datasets which will include intruder detection that can be occurring anywhere on the earth. This has become very important to protect the data from intruders, for this intruder detection should be the most important key to get identified. If we don't know who is the intruder, then how we will get to know who is stealing the data which has been secured by using much biometric security, fingerprints, passwords, OTPs etc. intruder detection has become very important, especially on mobile objects - airplanes, ships, etc. As we know the problem then only we are able to find the solution. To prevent this, we are using the methods from machine learning, biometric recognition, data learning or hybrid methods. These are going to be the handle of the system that can help to secure the data from intruders by using best optimization techniques to get precise data. We proposed a model for the banking system, whereby using biometric impressions and digital signatures to make every transaction possible by bank's customer. This proposes the security for the Smart Online Banking System (SOBS) by using the biometric prints, it can become more secure and reduces a lot of threats can be made by an intruder.