Name
Title | ||
|---|---|---|
KeyNet: Enhancing Cybersecurity with Deep Learning-Based LSTM on Keystroke Dynamics for Authentication |
Abstract | ||
|---|---|---|
Currently, everyone accumulates, stores, and processes their sensitive data on computers which makes it essential to protect computers from intrusion. Several approaches employ biometric data such as voice, retinal scan, fingerprints, etc., to enhance user authentication. There is an added overhead of sensors needed to implement these biometric approaches. Instead, an improved and strong password authentication would be cost-effective and straightforward. Keystroke dynamics is the analysis of temporal patterns to validate user authenticity. It is a behavioral biometric that makes use of the typing style of an individual and can be used to enhance the current authentication security procedures efficiently and economically. Such a behavioral biometric system is fairly unexplored compared to other behavioral verifications models. In this study, we focus on applying and training deep learning approach based Long Short Term Memory (LSTM) algorithm in an optimized way to validate temporal keystroke patterns of users for improved password authentication. Our research shows an enhanced authentication rate for the keystroke dynamic benchmark dataset. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-98404-5_67 | Intelligent Human Computer Interaction |
Keywords | DocType | Volume |
Keystroke dynamics, Long short term memory, Password authentication | Conference | 13184 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jayesh Soni | 1 | 0 | 1.01 |
| Nagarajan Prabakar | 2 | 62 | 6.28 |