Neural network models in big data analytics and cyber security
| Autor: | Ana-Maria Ghimes, Victor-Valeriu Patriciu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
business.industry Computer science Big data 020206 networking & telecommunications 02 engineering and technology Overfitting Computer security computer.software_genre Popularity Domain (software engineering) Data modeling 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pruning (decision trees) business computer |
| Zdroj: | 2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). |
| DOI: | 10.1109/ecai.2017.8166441 |
| Popis: | Nowadays, big data analytics has gained more popularity than any other domain in the research world. Its uses in domains like cyber security, but also the security of data itself, represent great challenges for researchers. Neural Network approaches have been of interest in developing models and architectures for discovering patterns and malicious activity of the users. In certain cases, the right model of a neural network can give better results than any other approach used for the same purpose. The pruning techniques for avoiding underfitting or overfitting and the training strategies can improve performance and deliver the best results. |
| Databáze: | OpenAIRE |
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