User Authentication And Identification Using Neuralnetwork
Md · i-manager’s Journal on Pattern Recognition · 2015
User authentication and identification these days have become a vital issue in preventing unauthorized access of information. Traditional user ID and password scheme have failed to prevent secure information from intruders because it has many flaws such as Dictionary attack, Brute Force attack, Phishing, Shoulder surfing, Spyware, Social Engineering, Guessing, etc. Biometric-based authentication techniques provide more reliable and efficient way of information security. Keystroke dynamics is one of the strong behavioral biometric based authentication system that can extract data and analyze the way a user types. It has made the authentication process simpler and smoother. Statistical models such as Mean, KNN, t-Test, Bayesian, etc were found to be the first techniques used to analyze keystroke biometrics and dominated for couple of years. Introducing simple Multilayer Perceptron (MLP) with Back Propagation (BP) neural network approach in keystroke dynamics by Brown and Rogers (Brown & Rogers, 1993) gained much attention because of promising experimental result. After that a number of researches have been conducted in keystroke dynamics using different neural networks and achieved higher accuracy rate. Some researchers have also claimed that neural network can produce better result than the statistical methods. In keystroke dynamics, input sample data are iteratively fed into the network to produce outputs based on its initial predetermined weights. This output is then compared with true output to compute error value. The error value is propagated backwards through the network to recalculate the weights at each hidden layer so that the error value can be reduced. These processes continue until the error value falls below the predefined threshold. Although Neural Network gained so much attention for classification, nowadays other machine learning approaches are widely used by the researchers. This is one of the motivations for surveying researches in keystroke dynamics those have used neural networks for authentication and identification. Keystroke dynamic systems have low accuracy compared to other biometric