05-05-2012, 11:52 AM
User Input Pattern-Based Authentication Method to Prevent Mobile E-Financial Incidents
User Input Pattern1.docx (Size: 15.25 KB / Downloads: 28)
Introduction:
Android is a software stack for mobile devices that includes an operating system, middleware and key applications. The Android SDK provides the tools and APIs necessary to begin developing applications that run on Android-powered devices. With the help of this application we proposed a novel approach to prevent e-financial incidents by analyzing the input patterns of mobile banking users such as how long it takes a user to input data into a mobile device, and the normal finger pressure levels when using a touch screen and distinguish the differences between an original user's usage pattern and an attacker's usage pattern.
Description:
In this application we can prevent the financial incidents by analyzing the input patterns of mobile banking users.
Existing System:
There are many applications available on web, but all applications are focused on fraud identification instead of fraud prevention, which means that actions are taken only after a fraud occurs instead of performing a series of preventive procedures.
Proposed System:
The proposed method shows high accuracy and is effective in proactive preventing e-financial incidents. In this we can distinguish the differences between an original user's usage pattern and an attacker's usage pattern. If the same account is used by radically different profiles an alert is flagged to human customer relationship executive who will then call the original user for confirmation.
Modules:
Module1: The first module whenever the user logs in for the first time for mobile net banking, the user credentials are retrieved to check whether the user is an authorized user or not, after that it will ask to enter the new password then the details are stored in database.
Module2: In this module we will analyze the user input pattern i.e.; the order in which the fields are entered and details are stored in database.
Module3: In this module we will checks the user input pattern that are matches with existing user input patterns. If the user input pattern is matched it will identify the user is authenticated otherwise we will provide the security question.
Module4: In this module whenever the answer for the security question is wrong then immediately the human customer relationship executive will get an alert ,then he calls to original user for a confirmation.