Keystroke Dynamics authentication using Data Fiitering Techniques and Neurai Network Approaches
| dc.category | Book Chapter | |
| dc.contributor.author | Shanmugapriya, D | |
| dc.date.accessioned | 2017-04-19T23:09:45Z | |
| dc.date.available | 2017-04-19T23:09:45Z | |
| dc.date.issued | 2010 | |
| dc.department | Information Technology | en_US |
| dc.description.abstract | bcuring the secret data and computer systems by allowing ase access only to the authenticated users and enduring the ^ k s of imposters is one of the major challenges in the of computer security. Traditionally, user name and ■sssvord schemes are widely used for controlling the access B computer systems. But, this scheme has many flaws such B Password sharing. Shoulder surfing. Brute force attack, ISctionary attack, Guessing, Phishing and many more. Sametrics technologies provide more reliable and efficient »52ns of authentication and verification. Keystroke S^namics is one of the famous biometric technologies, i ^ h will try to identify the authentication of a user when be user is working with a keyboard. In this paper, neural Wnork approaches with keystrokes for three different jBss>N'ords namely weak, medium and strong passwords are Bten into consideration. Neural Network algorithms are azended by applying normalization techniques for data fflsring before performing the classification on the datasets. iSe performance of normalization based neural network ^ rithm s is compared against neural network algorithms all different category of passwords and the accuraey lijtained is compared. | en_US |
| dc.identifier.uri | https://ir.avinuty.ac.in/handle/avu/3295 | |
| dc.lang | English | en_US |
| dc.publisher.name | Springer Verlag Berlin Heideberg | en_US |
| dc.publisher.type | National | en_US |
| dc.title | Keystroke Dynamics authentication using Data Fiitering Techniques and Neurai Network Approaches | en_US |
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