General Regression Neural Network Approach for Image Transformation Based Hybrid Graphical Password Authentication System

In this digital generation, computer, and information security plays a prominent role for both individuals and business organizations. In this interconnected business environment, information is the most valuable asset and it is of utmost importance to both individuals and organizations. The task of protecting information can be achieved through authentication. Today, textual password authentication is with username and password combination commonly used for many web applications. But textual passwords are the weakest form of authentication and it is easily guessed by the attacker by applying the various techniques such as brute force, dictionary attack, etc. To provide security from vulnerable attacks, graphical passwords are another alternative authentication mechanism for replacing the textual passwords.  This paper proposes image transformation based hybrid graphical password authentication model utilizes general regression neural network model and feature extraction methods for user identification. Three types of image transformations such as normal image, mirror image and shift image are considered to enhance security. In this paper, three types of feature extraction techniques such as SURF, LBP and HOG are considered for extracting image features. The performance of the proposed model is analysed, in terms of usability, security and storage space analysis and the results proved that the proposed system is resistant against various attacks like brute force, dictionary attack, shoulder surfing etc.