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- Title
LSTM deep learning method for network intrusion detection system.
- Authors
Boukhalfa, Alaeddine; Abdellaoui, Abderrahim; Hmina, Nabil; Chaoui, Habiba
- Abstract
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.
- Subjects
LONG-term memory; SHORT-term memory; MACHINE learning; DEEP learning; COMPUTER network security
- Publication
International Journal of Electrical & Computer Engineering (2088-8708), 2020, Vol 10, Issue 3, p3315
- ISSN
2088-8708
- Publication type
Article
- DOI
10.11591/ijece.v10i3.pp3315-3322