• S Rao Chintalapudi
• Sri Sai Vinay Garapati
• A Daniel Praveen Kumar
• K Sri Sai Ram

The majority of computer systems employ a login ID and password as the principal method for access security. In stand-alone situations, this level of security may be adequate, but when computers are connected to the internet, the vulnerability to a security breach is increased. In order to reduce vulnerability to attack, biometric solutions have been employed. In this paper, we investigate the use of a behavioural biometric based on keystroke dynamics. Although there are several implementations of keystroke dynamics available, their effectiveness is variable and dependent on the data sample and its acquisition methodology. The results from this study indicate that the accuracy is significantly influenced by the attribute selection process and to a lesser extent on the authentication algorithm employed. Our results also provide evidence that Multi Layer Perceptron(MLP) Classifier is more accurate compared to K- Nearest Neighbor (KNN) and Support Vector Machine (SVM).

K-Nearest Neighbor, Support Vector Machine, Multi Layer Perceptron, Keystroke Dynamics

"A Machine Learning Approach for User Identification using Keystroke Dynamics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.521-526, April-2020, Available :http://www.jetir.org/papers/JETIR2004273.pdf

"A Machine Learning Approach for User Identification using Keystroke Dynamics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp521-526, April-2020, Available at : http://www.jetir.org/papers/JETIR2004273.pdf