LSTM deep learning method for network intrusion detection system
| Main Authors: | Alaeddine Boukhalfa, Abderrahim Abdellaoui, Nabil Hmina, Habiba Chaoui |
|---|---|
| Format: | Article |
| Terbitan: |
, 2020
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4137948 |
Daftar Isi:
- The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services, these various attacks push to think of a new way to block them all in one manner. In addition, these intrusions can change and penetrate the devices of security. To solve these issues, we suggest, in this paper, a new idea for Network Intrusion Detection System (NIDS) based on Long Short-Term Memory (LSTM) to recognize menaces and to obtain a long-term memory on them, in order to stop the new attacks that are like the existing ones, and at the same time, to have a single mean to block intrusions. According to the results of the experiments of detections that we have realized, the Accuracy reaches up to 99.98 % and 99.93 % for respectively the classification of two classes and several classes, also the False Positive Rate (FPR) reaches up to only 0,068 % and 0,023 % for respectively the classification of two classes and several classes, which proves that the proposed model is effective, it has a great ability to memorize and differentiate between normal traffic and attacks, and its identification is more accurate than other Machine Learning classifiers.