A Deep Learning Approach for Intrusion Detection using Recurrent Neural Network
| dc.contributor.author | Rai Dipendra | |
| dc.date.accessioned | 2022-04-15T07:38:39Z | |
| dc.date.available | 2022-04-15T07:38:39Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Intrusion detection discover a critical part in guaranteeing data security and the key innovation is to precisely recognize different assaults in the system. In this dissertation, the intrusion detection model based on deep learning is investigated, and a deep learning approach for intrusion detection using recurrent neural networks (RNN-IDS) is used. The performance of the model is based on binary and multiclass classification, and the number of neurons and different learning rate impacts on the performance of the model has been studied. The performance of the model is compared with Naïve Bayes, Multilayer Perceptron and Support Vector Machine that has been analyzed by previous researchers on the benchmark data set. The test results demonstrate that RNN-IDS is remarkably appropriate for displaying high precision and its execution is better than that of machine learning techniques in both binary and multiclass classification. The RNN-IDS demonstrate enhances the precision of the intrusion identification and gives other examination strategy to intrusion detection discovery. Keywords: Recurrent neural networks, RNN-IDS, intrusion detection, deep learning, machine learning. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.14540/9841 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Department of computer Science & information Technology | en_US |
| dc.subject | Recurrent neural networks | en_US |
| dc.subject | Intrusion detection | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | machine learning. | en_US |
| dc.title | A Deep Learning Approach for Intrusion Detection using Recurrent Neural Network | en_US |
| dc.type | Thesis | en_US |
| local.academic.level | Masters | en_US |
| local.institute.title | Central Department of Computer Science and Information Technology | en_US |