Machine Learning Algorithm on Keystroke Dynamics Pattern
| Autor: | Maleika Heenaye-Mamode Khan, Purvashi Baynath, K. M. Sunjiv Soyjaudah |
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| Rok vydání: | 2018 |
| Předmět: |
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050210 logistics & transportation Neuroevolution Artificial neural network Fuzzy expert system Computer science business.industry Chaotic neural network 05 social sciences Topology (electrical circuits) 02 engineering and technology Machine learning computer.software_genre Support vector machine Keystroke dynamics 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Algorithm |
| Zdroj: | 2018 IEEE Conference on Systems, Process and Control (ICSPC). |
| DOI: | 10.1109/spc.2018.8704135 |
| Popis: | In this paper, the machine learning algorithms have been applied on distinct features of Keystroke Dynamics. The Machine learning is important to correctly authenticate an individual. In this work, the complex models and algorithms help to determine when the person is a genuine user or an imposter through learning. The algorithms that has been studied and deployed,are the Fuzzy Expert System (FESs), NeuroEvolution of the augmenting topology (NEAT), Proposed NeuroEvolution of the augmenting topology, Support Vector Machine (SVM) and Chaotic Neural Network. From the algorithms applied, the proposed NEAT algorithms performs better in terms of recognition rate on both databases used where the recognition rate achieved above 95.6%. |
| Databáze: | OpenAIRE |
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