Open AccessLSTM deep learning method for network intrusion detection system
Author(s) -
Alaeddine Boukhalfa,
Abderrahim Abdellaoui,
Nabil Hmina,
Habiba Chaoui
Publication year - 2020
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v10i3.pp3315-3322
Subject(s) - computer science , memorization , intrusion detection system , block (permutation group theory) , intrusion , artificial intelligence , identification (biology) , computer security , network security , machine learning , pattern recognition (psychology) , mathematics , botany , geometry , mathematics education , geochemistry , biology , geology
The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services or steal information, 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-TermMemory (LSTM) to recognize menaces and to obtain a long-term memory on them, inorder to stop the new attacks that are like the existing ones, and at the sametime, to have a single mean to block intrusions. According to the results of the experiments of detections that we have carried out, the Accuracy reaches upto 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 very effective, it has a great ability to memorize and differentiate between normal traffic and attack traffic and its identification is more accurate than other Machine Learning classifiers.