Anomaly Intrusion Detection System Using Neural Network
| dc.contributor.advisor | Fadi , Draidi | |
| dc.contributor.author | Raghad, Bashir | |
| dc.contributor.author | Hanan, Rayyan | |
| dc.contributor.author | Mohammad, Khateeb | |
| dc.date.accessioned | 2021-01-24T11:27:54Z | |
| dc.date.available | 2021-01-24T11:27:54Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | security has always been a challenge for administrators, as bay by day attacks became sophisticated. which make it harder for Intrusion detection systems (IDS) to detect, there are three types of IDS, signature-based, anomaly-based, IDSs use signature-based and anomaly-based is known as hybrid. Many researchers are focused on anomaly-based due to its ability to detect zero-day-attack, but there are some limitations such as false positive alarms problem. By using Arti cial Neural Networks (ANN), the process of designing and testing AIDS is easier. In this paper, the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) neural network are used to reduce the number of false alarms, by using Keras, sklearn API, and TensorFlow with CSE-CIC-IDS2017, 2018 datasets for training and testing the AIDS model. In our research, we found that the accuracies of CNN and LSTM neural networks are 93% and 99% respectively, and CNN is better in multi-classi cation while LSTM is better in binary-classi cation. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.11888/15579 | |
| dc.language.iso | en_US | en_US |
| dc.title | Anomaly Intrusion Detection System Using Neural Network | en_US |
| dc.type | Graduation Project | en_US |
Files
License bundle
1 - 1 of 1
Loading...
- Name:
- license.txt
- Size:
- 1.71 KB
- Format:
- Item-specific license agreed upon to submission
- Description: